Model Analysis
Nikhil Gupta
2019-02-11 23:45:45
sessionInfo()## R version 3.5.2 (2018-12-20)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 17763)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252
## [2] LC_CTYPE=English_United States.1252
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## loaded via a namespace (and not attached):
## [1] compiler_3.5.2 magrittr_1.5 tools_3.5.2 htmltools_0.3.6
## [5] yaml_2.2.0 Rcpp_1.0.0 stringi_1.2.4 rmarkdown_1.11
## [9] knitr_1.21 stringr_1.3.1 xfun_0.4 digest_0.6.18
## [13] evaluate_0.12User Inputs
output.var = params$output.var
transform.abs = params$transform.abs
log.pred = params$log.pred
norm.pred = params$norm.pred
eda = params$eda
algo.forward = params$algo.forward
algo.backward = params$algo.backward
algo.stepwise = params$algo.stepwise
algo.LASSO = params$algo.LASSO
algo.LARS = params$algo.LARS
algo.forward.caret = params$algo.forward.caret
algo.backward.caret = params$algo.backward.caret
algo.stepwise.caret = params$algo.stepwise.caret
algo.LASSO.caret = params$algo.LASSO.caret
algo.LARS.caret = params$algo.LARS.caret
message("Parameters used for training/prediction: ")## Parameters used for training/prediction:str(params)## List of 15
## $ output.var : chr "y3"
## $ transform.abs : logi FALSE
## $ log.pred : logi FALSE
## $ norm.pred : logi TRUE
## $ eda : logi FALSE
## $ algo.forward : logi FALSE
## $ algo.backward : logi FALSE
## $ algo.stepwise : logi FALSE
## $ algo.LASSO : logi FALSE
## $ algo.LARS : logi FALSE
## $ algo.forward.caret : logi TRUE
## $ algo.backward.caret: logi TRUE
## $ algo.stepwise.caret: logi TRUE
## $ algo.LASSO.caret : logi TRUE
## $ algo.LARS.caret : logi TRUE# Setup Labels
# alt.scale.label.name = Alternate Scale variable name
# - if predicting on log, then alt.scale is normal scale
# - if predicting on normal scale, then alt.scale is log scale
if (log.pred == TRUE){
label.names = paste('log.',output.var,sep="")
alt.scale.label.name = output.var
}
if (log.pred == FALSE & norm.pred==FALSE){
label.names = output.var
alt.scale.label.name = paste('log.',output.var,sep="")
}
if (norm.pred==TRUE){
label.names = paste('norm.',output.var,sep="")
alt.scale.label.name = output.var
}Prepare Data
Read and Clean Features
features = read.csv("../../Data/features.csv")
features.highprec = read.csv("../../Data/features_highprec.csv")all.equal(features, features.highprec)## [1] "Component \"x11\": Mean relative difference: 0.001401482"
## [2] "Component \"stat9\": Mean relative difference: 0.0002946299"
## [3] "Component \"stat12\": Mean relative difference: 0.0005151515"
## [4] "Component \"stat13\": Mean relative difference: 0.001354369"
## [5] "Component \"stat18\": Mean relative difference: 0.0005141104"
## [6] "Component \"stat22\": Mean relative difference: 0.001135977"
## [7] "Component \"stat25\": Mean relative difference: 0.0001884615"
## [8] "Component \"stat29\": Mean relative difference: 0.001083691"
## [9] "Component \"stat36\": Mean relative difference: 0.00021513"
## [10] "Component \"stat37\": Mean relative difference: 0.0004578125"
## [11] "Component \"stat43\": Mean relative difference: 0.0003473684"
## [12] "Component \"stat45\": Mean relative difference: 0.0002951699"
## [13] "Component \"stat46\": Mean relative difference: 0.0009745763"
## [14] "Component \"stat47\": Mean relative difference: 8.829902e-05"
## [15] "Component \"stat55\": Mean relative difference: 0.001438066"
## [16] "Component \"stat57\": Mean relative difference: 0.0001056911"
## [17] "Component \"stat58\": Mean relative difference: 0.0004882261"
## [18] "Component \"stat60\": Mean relative difference: 0.0002408377"
## [19] "Component \"stat62\": Mean relative difference: 0.0004885106"
## [20] "Component \"stat66\": Mean relative difference: 1.73913e-06"
## [21] "Component \"stat67\": Mean relative difference: 0.0006265823"
## [22] "Component \"stat73\": Mean relative difference: 0.003846154"
## [23] "Component \"stat75\": Mean relative difference: 0.002334906"
## [24] "Component \"stat83\": Mean relative difference: 0.0005628415"
## [25] "Component \"stat86\": Mean relative difference: 0.0006104418"
## [26] "Component \"stat94\": Mean relative difference: 0.001005115"
## [27] "Component \"stat97\": Mean relative difference: 0.0003551913"
## [28] "Component \"stat98\": Mean relative difference: 0.0006157635"
## [29] "Component \"stat106\": Mean relative difference: 0.0008267717"
## [30] "Component \"stat109\": Mean relative difference: 0.0005121359"
## [31] "Component \"stat110\": Mean relative difference: 0.0007615527"
## [32] "Component \"stat111\": Mean relative difference: 0.001336134"
## [33] "Component \"stat114\": Mean relative difference: 7.680492e-05"
## [34] "Component \"stat117\": Mean relative difference: 0.0002421784"
## [35] "Component \"stat122\": Mean relative difference: 0.0006521084"
## [36] "Component \"stat123\": Mean relative difference: 8.333333e-05"
## [37] "Component \"stat125\": Mean relative difference: 0.002385135"
## [38] "Component \"stat130\": Mean relative difference: 0.001874016"
## [39] "Component \"stat132\": Mean relative difference: 0.0003193182"
## [40] "Component \"stat135\": Mean relative difference: 0.0001622517"
## [41] "Component \"stat136\": Mean relative difference: 7.722008e-05"
## [42] "Component \"stat138\": Mean relative difference: 0.0009739953"
## [43] "Component \"stat143\": Mean relative difference: 0.0004845361"
## [44] "Component \"stat146\": Mean relative difference: 0.0005821596"
## [45] "Component \"stat148\": Mean relative difference: 0.0005366922"
## [46] "Component \"stat153\": Mean relative difference: 0.0001557522"
## [47] "Component \"stat154\": Mean relative difference: 0.001351916"
## [48] "Component \"stat157\": Mean relative difference: 0.0005427928"
## [49] "Component \"stat162\": Mean relative difference: 0.002622951"
## [50] "Component \"stat167\": Mean relative difference: 0.0005905172"
## [51] "Component \"stat168\": Mean relative difference: 0.0002791096"
## [52] "Component \"stat169\": Mean relative difference: 0.0004121827"
## [53] "Component \"stat170\": Mean relative difference: 0.0004705882"
## [54] "Component \"stat174\": Mean relative difference: 0.0003822894"
## [55] "Component \"stat179\": Mean relative difference: 0.0008286604"
## [56] "Component \"stat184\": Mean relative difference: 0.0007526718"
## [57] "Component \"stat187\": Mean relative difference: 0.0005122768"
## [58] "Component \"stat193\": Mean relative difference: 4.215116e-05"
## [59] "Component \"stat199\": Mean relative difference: 0.002155844"
## [60] "Component \"stat203\": Mean relative difference: 0.0003738318"
## [61] "Component \"stat213\": Mean relative difference: 0.000667676"
## [62] "Component \"stat215\": Mean relative difference: 0.0003997955"head(features)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.05e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.03e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.06e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.47e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.01e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.07e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074head(features.highprec)## JobName x1 x2 x3 x4 x5 x6
## 1 Job_00001 2.0734508 4.917267 19.96188 3.520878 7.861051 1.6067589
## 2 Job_00002 2.2682543 4.955773 19.11939 19.763031 6.931355 1.3622041
## 3 Job_00003 1.7424456 2.059819 13.37912 38.829132 6.274053 2.0529845
## 4 Job_00004 0.7873555 2.613983 17.23044 64.402557 5.377652 0.9067419
## 5 Job_00005 2.3342753 4.299076 14.64883 52.537304 6.793368 2.4605792
## 6 Job_00006 1.2365089 2.795370 11.13127 96.819939 6.583971 2.3510606
## x7 x8 x9 x10 x11 x12 x13
## 1 2.979479 8.537228 1.103368 4.6089458 1.050025e-07 7.995825 13.215498
## 2 2.388119 6.561461 0.588572 1.0283282 1.034518e-07 7.486966 22.557224
## 3 2.043592 10.275595 4.834385 4.3872848 1.062312e-07 6.350142 15.049810
## 4 2.395118 13.487331 3.340190 4.5053501 9.471887e-08 9.548698 17.170635
## 5 2.891535 9.362389 1.246039 1.7333300 1.010552e-07 9.596095 5.794567
## 6 1.247838 7.033354 1.852231 0.4839371 1.071662e-07 3.810983 23.863169
## x14 x15 x16 x17 x18 x19 x20
## 1 4.377983 0.2370623 6.075459 3.988347 4.767475 2.698775 1.035893
## 2 2.059315 0.5638121 6.903891 4.152054 6.849232 9.620731 1.915288
## 3 3.260057 2.0603445 8.424065 4.489893 3.493591 4.715386 1.558103
## 4 3.093478 1.8806034 11.189792 2.134271 5.588357 5.107871 1.489588
## 5 3.943076 1.5820830 7.096742 3.563378 7.765610 1.360272 1.240283
## 6 1.280562 1.1733382 7.062051 1.341864 7.748325 5.009365 1.725179
## x21 x22 x23 stat1 stat2 stat3 stat4
## 1 42.36548 1.356213 2.699796 2.3801832 0.1883335 -1.2284011 -0.5999233
## 2 26.63295 4.053961 2.375127 -1.4069480 1.8140973 1.6204884 2.6422672
## 3 20.09693 3.079888 4.488420 -0.7672566 -0.1230289 1.1415752 2.9805934
## 4 32.60415 1.355396 3.402398 0.4371202 -1.9355906 0.9028624 -1.6025400
## 5 44.58361 1.940301 2.249011 2.4492466 -0.6172000 -2.5520642 -2.1485929
## 6 28.75102 2.500499 5.563972 -1.7899084 1.8853619 2.4154840 -2.6022179
## stat5 stat6 stat7 stat8 stat9 stat10
## 1 0.148893163 -0.6622978 -2.4851868 0.3647782 2.5364335 2.92067981
## 2 1.920768980 1.7411555 -1.9599979 -2.0190558 -1.3732762 -0.31642506
## 3 2.422584300 -0.4166040 2.2205689 -2.6741531 0.4844292 2.73379230
## 4 -0.001795933 -0.6946563 -0.3693534 -0.9709467 1.7960306 0.74771154
## 5 -2.311132430 -1.0166832 2.7269876 1.5424492 -1.3156369 -0.09767897
## 6 -1.785491470 -1.8599915 1.4875095 2.0188572 -1.4892503 -1.41103566
## stat11 stat12 stat13 stat14 stat15 stat16
## 1 -2.3228905 -2.480567 -0.6335157 -0.3650149 -0.5322812 0.6029300
## 2 -0.8547903 1.119316 0.7227427 0.2121097 -0.1452281 -2.0361528
## 3 -2.1821580 2.865401 -2.9756081 2.9871745 1.9539525 -1.8857163
## 4 1.3982378 1.856765 -1.0379983 2.3341896 2.3057184 -2.8947697
## 5 0.9567220 2.567549 0.3184886 1.0307668 0.1644241 -0.6613821
## 6 0.5341771 -1.461822 0.4402476 -1.9282095 -0.3680157 1.8188807
## stat17 stat18 stat19 stat20 stat21 stat22
## 1 -1.04516208 2.3544915 2.4049001 0.2633883 -0.9788178 1.7868229
## 2 0.09513074 0.4727738 1.8899702 2.7892542 -1.3919091 -1.7198164
## 3 0.40285346 1.4655282 -1.4952788 2.9162340 -2.3893208 2.8161423
## 4 2.97446084 2.3896182 2.3083484 -1.1894441 -2.1982553 1.3666242
## 5 -0.98465055 0.6900643 1.5894209 -2.1204538 1.7961155 -0.9362189
## 6 -1.45726359 -2.1139548 -0.3964904 1.1764175 -2.9100556 -2.1359294
## stat23 stat24 stat25 stat26 stat27 stat28
## 1 -2.3718851 2.8580718 -0.4719713 -2.817086 -0.9518474 2.88892484
## 2 -2.3293245 1.5577759 -1.9569720 1.554194 -0.5081459 -1.58715141
## 3 -2.5402296 0.1422861 0.3572798 -1.051886 -2.1541717 0.03074004
## 4 -1.9679050 -1.4077642 2.5097435 1.683121 -0.2549745 -2.90384054
## 5 2.0523429 -2.2084844 -1.9280857 -2.116736 1.8180779 -1.42167580
## 6 0.2184991 -0.7599817 2.6880329 -2.903350 -1.0733233 -2.92416644
## stat29 stat30 stat31 stat32 stat33 stat34
## 1 0.7991088 -2.0059092 -0.2461502 0.6482101 -2.87462163 -0.3601543
## 2 1.9758110 -0.3874187 1.3566630 2.6493473 2.28463054 1.8591728
## 3 -0.4460218 1.0279679 1.3998452 -1.0183365 1.41109037 -2.4183984
## 4 1.0571996 2.5588036 -2.9830337 -1.1299983 0.05470414 -1.5566561
## 5 0.8854889 2.2774174 2.6499031 2.3053405 -2.39148426 -1.8272992
## 6 -0.8405267 0.1311945 0.4321289 -2.9622040 -2.55387473 2.6396458
## stat35 stat36 stat37 stat38 stat39 stat40
## 1 2.4286051 -0.5420244 -2.6782637 -2.8874269 -0.8945006 1.1749642
## 2 1.3709245 -1.3714181 1.3901204 1.2273489 -0.8934880 1.0540369
## 3 -0.9805572 2.0571353 0.8845031 2.0574493 1.1222047 1.8528618
## 4 1.0969149 -2.2820673 1.8852408 0.5391517 2.7334342 -0.4372566
## 5 -1.0971669 1.4867796 -2.3738465 -0.3743561 1.4266498 1.2551680
## 6 0.4584349 -2.2696617 -0.9935142 -0.5350499 -0.7874799 2.0009417
## stat41 stat42 stat43 stat44 stat45 stat46
## 1 -1.0474428 -1.3909023 2.54110503 -1.4320793 0.6298335 -2.09296608
## 2 2.5380247 1.6476108 0.44128850 -2.5049477 1.2726039 1.72492969
## 3 1.1477574 0.2288794 0.08891252 2.3044751 -0.7735722 -0.07302936
## 4 -1.3808300 -2.7900956 2.38297582 0.1686397 -2.1591296 1.60828602
## 5 0.2257536 1.9542116 2.66429019 0.8026123 -1.5521187 1.61751962
## 6 -1.3364114 -2.2898803 2.80735397 -0.8413086 1.0057797 -1.50653386
## stat47 stat48 stat49 stat50 stat51 stat52
## 1 -2.8318939 2.1445766 0.5668035 0.1544579 0.6291955 2.2197027
## 2 -0.5804687 -1.3689737 1.4908396 1.2465997 0.8896304 -2.6024318
## 3 0.7918019 1.5712964 1.1038082 -0.2545658 -2.1662638 0.2660159
## 4 -1.8894132 0.5680230 -0.7023218 -0.3972188 0.1578027 2.1770194
## 5 2.1088455 -2.7195437 2.1961412 -0.2615084 1.2109556 0.8260623
## 6 -1.4400891 -0.9421459 -1.7324599 -2.1720727 -2.8129435 0.6958785
## stat53 stat54 stat55 stat56 stat57 stat58
## 1 2.176805 0.5546907 -2.19704103 -0.2884173 1.3232913 -1.32824039
## 2 -2.107441 1.3864788 0.08781975 1.9998228 0.8014438 -0.26979154
## 3 1.234197 2.1337581 1.65231645 -0.4388691 -0.1811156 2.11277962
## 4 2.535406 -2.1387620 0.12856023 -1.9906180 0.9626449 1.65232646
## 5 -2.457080 2.1633499 0.60441124 2.5449364 -1.4978440 2.60542655
## 6 2.003033 -0.5379940 -2.19647264 -1.1954677 -0.5974466 -0.04703835
## stat59 stat60 stat61 stat62 stat63 stat64
## 1 1.24239659 -2.5798278 1.327928 1.68560362 0.6284891 -1.6798652
## 2 0.06379301 0.9465770 1.116928 0.03128772 -2.1944375 0.3382609
## 3 0.93223447 2.4597080 0.465251 -1.71033382 -0.5156728 1.8276784
## 4 -0.29840910 0.7273473 -2.313066 -1.47696018 2.5910559 -1.5127999
## 5 -1.17610002 -1.7948418 -2.669305 0.17813617 2.8956099 2.9411416
## 6 -1.01793981 0.2817057 2.228023 -0.86494124 -0.9747949 -0.1569053
## stat65 stat66 stat67 stat68 stat69 stat70
## 1 -2.9490898 -0.3325469 1.5745990 -2.2978280 1.5451891 -1.345990
## 2 -1.1174885 -1.5728682 -2.9229002 0.2658547 -1.9616533 2.506130
## 3 -0.2231264 -0.4503301 0.7932286 -1.2453773 -2.2309763 2.309761
## 4 -0.3522418 -2.0720532 0.9442933 2.9212906 0.5100371 -2.441108
## 5 -2.1648991 1.2002029 2.8266985 0.7461294 1.6772674 -1.280000
## 6 -2.2295458 1.1446493 0.2024925 -0.2983998 -2.8203752 1.224030
## stat71 stat72 stat73 stat74 stat75 stat76
## 1 1.0260956 2.1071210 2.6625669 -2.8924677 -0.02132523 -2.5058765
## 2 0.3525076 1.6922342 -1.2167022 -1.7271879 2.21176434 1.9329683
## 3 -2.1799035 -2.2645276 0.1415582 0.9887453 1.95592320 0.2951785
## 4 -2.4051409 2.0876484 -0.8632146 0.4011389 -1.16986716 -1.2391174
## 5 1.3538754 -0.8089395 -0.5122626 -2.1696892 1.07344925 2.6696169
## 6 -2.8073371 -1.4450488 0.5481212 -1.4381690 0.80917043 -0.1365944
## stat77 stat78 stat79 stat80 stat81 stat82
## 1 -2.5631845 -2.40331340 0.38416120 -1.2564875 -0.1550840 -1.1762617
## 2 -0.4462085 0.38400793 1.80483031 -0.8387642 0.7624431 0.9936900
## 3 1.6757870 -1.81900752 2.70904708 -0.3201959 2.5754235 1.6346260
## 4 -2.1012006 -2.24691081 1.78056848 1.0323739 1.0762523 2.1343851
## 5 -2.5736733 -1.99958372 -0.05388495 -2.5630073 -2.8783002 -0.5752426
## 6 1.6143972 0.03233746 2.90835762 1.4000487 2.9275615 -2.8503830
## stat83 stat84 stat85 stat86 stat87 stat88
## 1 1.2840565 -2.6794965 1.3956039 -1.5290235 2.221152 2.3794982
## 2 -0.2380048 1.9314318 -1.6747955 -0.3663656 1.582659 -0.5222489
## 3 -0.9150769 -1.5520337 2.4186287 2.7273662 1.306642 0.1320062
## 4 -2.5824408 -2.7775943 0.5085060 0.4689015 2.053348 0.7957955
## 5 -1.0017741 -0.2009138 0.3770109 2.4335201 -1.118058 1.3953410
## 6 2.4891765 2.9931953 -1.4171852 0.3905659 -1.856119 -2.1690490
## stat89 stat90 stat91 stat92 stat93 stat94
## 1 -0.9885110 -0.8873261 -2.7810929 -1.53325891 2.6002395 1.8890998
## 2 0.9982028 -1.2382015 -0.1574496 0.41086048 -0.5412626 -0.2421387
## 3 0.5956759 1.6871066 2.2452753 2.74279594 -1.5860478 2.9393122
## 4 2.0902634 2.1752586 -2.0677712 -2.37861037 1.1653302 0.1500632
## 5 2.9820614 0.8111660 -0.7842287 0.03766387 -1.1681970 2.1217251
## 6 -1.7428021 0.1579032 1.7456742 -0.36858466 -0.1304616 -1.4555819
## stat95 stat96 stat97 stat98 stat99 stat100
## 1 -2.6056035 -0.5814857 2.57652426 -2.3297751 2.6324007 1.445827
## 2 -2.0271583 -0.9126074 2.49582648 0.9745382 1.1339203 -2.549544
## 3 0.3823181 -0.6324139 2.46221566 1.1151560 0.4624891 0.107072
## 4 2.6414623 -0.6630505 2.10394859 1.2627635 0.4861740 1.697012
## 5 1.4642254 2.6485956 -0.07699547 0.6219473 -1.8815142 -2.685463
## 6 1.8937331 -0.4690555 1.04671776 -0.5879866 -0.9766789 2.405940
## stat101 stat102 stat103 stat104 stat105 stat106
## 1 -2.1158021 2.603936 1.7745128 -1.8903574 -1.8558655 1.0122044
## 2 -2.7998588 -2.267895 0.5336456 -0.2859477 -0.5196246 -0.9417582
## 3 0.7969509 -1.744906 -0.7960327 1.9767258 -0.2007264 -0.7872376
## 4 1.7071959 -1.540221 1.6770362 1.5395796 -0.4855365 -1.2894115
## 5 -1.4627420 -1.700983 2.4376490 0.2731541 1.5275587 1.3256483
## 6 2.6888530 1.090155 2.0769854 1.9615480 1.8689761 2.8975825
## stat107 stat108 stat109 stat110 stat111 stat112
## 1 1.954508 -0.3376471 2.503084 0.3099165 2.7209847 -2.3911204
## 2 -2.515160 0.3998704 -1.077093 2.4228268 -0.7759693 0.2513882
## 3 1.888827 1.5819857 -2.066659 -2.0008364 0.6997684 2.6157095
## 4 1.076395 -1.8524148 -2.689204 1.0985872 1.2389493 2.1018629
## 5 2.828866 -1.8590252 -2.424163 1.4391942 -0.6173239 -1.5218846
## 6 -1.419639 0.7888914 1.996463 0.9813507 0.9034198 1.3810679
## stat113 stat114 stat115 stat116 stat117 stat118
## 1 -1.616161 1.0878664 0.9860094 -0.06288462 -1.013501 -1.2212842
## 2 -1.554771 1.8683100 0.4880588 -0.63865489 -1.610217 -1.7713343
## 3 -2.679801 -2.9486952 1.7753417 0.90311784 -1.318836 -0.1429040
## 4 2.459229 -0.5584171 0.4419581 -0.09586351 0.595442 0.2883342
## 5 -2.102200 1.6300170 -2.3498287 1.36771894 -1.912202 -0.2563821
## 6 -1.835037 0.6577786 -2.9928374 2.13540316 -1.437299 -0.9570006
## stat119 stat120 stat121 stat122 stat123 stat124
## 1 2.9222729 1.9151262 1.6686068 2.0061224 1.5723072 0.78819227
## 2 2.1828208 0.8283178 -2.4458632 1.7133740 1.1393738 -0.07182054
## 3 0.9721319 1.2723130 2.8002086 2.7670381 -2.2252586 2.17499113
## 4 -1.9327896 -2.5369370 1.7835028 1.0262097 -1.8790983 -0.43639564
## 5 1.3230809 -2.8145256 -0.9547533 -2.0435417 -0.2758764 -1.85668027
## 6 0.1720700 -1.4568460 1.4115051 -0.9878145 2.3895061 -2.33730745
## stat125 stat126 stat127 stat128 stat129 stat130
## 1 1.588372 1.1620011 -0.2474264 1.650328 2.5147598 0.37283245
## 2 -1.173771 0.8162020 0.3510315 -1.263667 1.7245284 -0.72852904
## 3 -1.503497 -0.5656394 2.8040256 -2.139287 -1.7221642 2.17899609
## 4 1.040967 -2.9039600 0.3103742 1.462339 -1.2940350 -2.95015502
## 5 -2.866184 1.6885070 -2.2525666 -2.628631 1.8581577 2.80127025
## 6 -1.355111 1.5017927 0.4295921 -0.580415 0.9851009 -0.03773117
## stat131 stat132 stat133 stat134 stat135 stat136
## 1 -0.09028241 0.5194538 2.8478346 2.6664724 -2.0206311 1.398415090
## 2 -0.53045595 1.4134049 2.9180586 0.3299096 1.4784122 -1.278896090
## 3 1.35843194 0.2279946 0.3532595 0.6138676 -0.3443284 0.057763811
## 4 -1.92450273 1.2698178 -1.5299660 -2.6083462 1.1665530 -0.187791914
## 5 1.49036849 2.6337729 -2.3206244 0.4978287 -1.7397571 0.001200184
## 6 -0.64642709 -1.9256228 1.7032650 -0.9152725 -0.3188055 2.155395980
## stat137 stat138 stat139 stat140 stat141 stat142
## 1 -1.2794871 0.4064890 -0.4539998 2.6660173 -1.8375313 0.4711883
## 2 -2.7709017 -1.6303773 -1.9025910 0.2572918 0.6612002 1.4764348
## 3 -1.1930757 -0.1051243 -0.5108380 -1.0879666 2.4969513 -0.9477230
## 4 -1.2318919 2.2348571 0.1788580 -1.5851788 -1.2384283 -2.1859181
## 5 1.8685058 2.7229517 -2.9077182 2.6606939 -1.5963592 -2.2213492
## 6 -0.4807318 -1.2117369 -0.9358531 -2.5100758 -2.3803916 -0.7096854
## stat143 stat144 stat145 stat146 stat147 stat148
## 1 1.9466263 2.2689433 -0.3597288 -0.6551386 1.65438592 0.6404466
## 2 1.3156421 2.4459090 -0.3790028 1.4858465 -0.07784461 1.0096149
## 3 0.1959563 2.3062942 1.8459278 2.6848175 -2.70935774 -1.2093409
## 4 1.7633296 -2.8171508 2.0902622 -2.6625464 -1.12600601 -2.1926479
## 5 0.3885758 1.8160636 2.8257299 -1.4526173 1.60679603 2.3807991
## 6 0.7623450 0.2692145 -2.4307463 -2.1244523 -2.67803812 -1.5273387
## stat149 stat150 stat151 stat152 stat153 stat154
## 1 0.1583575 0.4755351 0.3213410 2.0241520 1.5720103 -0.1825875
## 2 -0.4311406 2.9577663 0.6937252 0.1397280 0.3775735 -1.1012636
## 3 -0.8352824 2.5716205 1.7528236 0.4326277 -2.2334397 -2.6265771
## 4 -2.8069143 1.8813509 2.3358023 0.1015632 1.2117474 -1.3714278
## 5 -1.6166265 1.1112266 -1.1998471 2.9316769 -2.1676455 -0.3411089
## 6 -0.2265472 2.7264354 -1.6746094 -2.3376281 -1.7022788 -1.2352397
## stat155 stat156 stat157 stat158 stat159 stat160
## 1 -1.139657 0.07061254 0.5893906 -1.9920996 -2.83714366 2.249398
## 2 -2.041093 0.74047768 2.5415072 -1.2697256 -1.64364433 -2.448922
## 3 -1.219507 -0.55198693 0.4046920 1.2098547 -0.90412390 -1.934093
## 4 2.992191 2.33222485 2.0622969 -0.6714653 2.76836085 -1.431120
## 5 -2.362356 -1.23906672 0.4746319 -0.7849202 0.69399995 2.052411
## 6 -1.604499 1.31051409 -0.5164744 0.6288667 0.07899523 -2.287402
## stat161 stat162 stat163 stat164 stat165 stat166
## 1 1.7182635 -1.2323593 2.7350423 1.0707235 1.1621544 0.9493989
## 2 -0.6247674 2.6740098 2.8211024 1.5561292 -1.1027147 1.0519739
## 3 -0.6230453 -0.7993517 -2.8318374 -1.1148673 1.4261659 0.5294309
## 4 1.7644744 0.1696584 1.2653207 0.6621516 0.9470508 0.1985014
## 5 -1.2070210 0.7243784 0.9736322 2.7426259 -2.6862383 1.6840212
## 6 2.3705316 -2.1667893 -0.2516685 -0.8425958 -1.9099342 -2.8607297
## stat167 stat168 stat169 stat170 stat171 stat172
## 1 0.1146510 2.3872008 1.1180918 -0.95370555 -2.25076509 0.2348182
## 2 1.0760417 -2.0449336 0.9715676 -0.40173489 -0.11953555 -2.3107369
## 3 1.1735898 1.3860190 -2.2894719 0.06350347 0.29191551 -1.6079744
## 4 2.5511832 0.5446648 1.2694012 -0.84571201 0.79789722 0.2623538
## 5 2.2900002 2.6289782 -0.2783571 1.39032829 -0.55532032 1.0499046
## 6 -0.7513983 2.9617066 -2.2119520 -1.71958113 -0.01452018 -0.2751517
## stat173 stat174 stat175 stat176 stat177 stat178
## 1 1.79366076 -1.920206 -0.38841942 0.8530301 1.64532077 -1.1354179
## 2 -0.07484659 1.337846 2.20911694 0.9616837 -2.80810070 -2.1136749
## 3 -1.05521810 -1.483741 0.06148359 2.3066039 -0.34688616 1.1840581
## 4 0.31460321 1.195741 2.97633862 1.1685091 -0.06346265 1.4205489
## 5 -1.39428365 2.458523 0.64836472 -1.0396386 -0.57828104 -0.5006818
## 6 2.31844401 1.239864 -2.06490874 0.7696204 -1.77586019 2.0855925
## stat179 stat180 stat181 stat182 stat183 stat184
## 1 2.0018647 0.1476815 -1.27279520 1.9181504 -0.5297624 -2.9718938
## 2 -2.1351449 2.9012582 -1.09914911 -2.5488517 -2.8377736 1.4073374
## 3 -1.7819908 2.9902627 0.81908613 0.2503852 0.3712984 -2.1714024
## 4 -0.1026974 -2.4763253 -2.52645421 1.3096315 2.1458161 -1.5228094
## 5 -2.2298794 2.4465680 -0.70346898 -1.6997617 2.9178164 -0.3615532
## 6 -1.1168108 1.5552123 -0.01361342 1.7338791 -1.1104763 0.1882416
## stat185 stat186 stat187 stat188 stat189 stat190
## 1 -0.1043832 -1.5047463 2.700351 -2.4780862 -1.9078265 0.9978108
## 2 -2.0310574 -0.5380074 -1.963275 -1.2221278 -2.4290681 -1.9515115
## 3 2.6727278 1.2688179 -1.399018 -2.9612138 2.6456394 2.0073323
## 4 -2.7796295 2.0682354 2.243727 0.4296881 0.1931333 2.2710960
## 5 -0.6231265 2.5833981 2.229041 0.8139584 1.4544131 1.8886451
## 6 2.7204690 -2.4469144 -1.421998 1.7477882 -0.1481806 0.6011560
## stat191 stat192 stat193 stat194 stat195 stat196
## 1 -0.6644351 2.6270833 -1.1094601 -2.4200392 2.870713 -0.6590932
## 2 -0.6483142 1.4519118 -0.1963493 -2.3025322 1.255608 2.1617947
## 3 -1.5457382 -0.2977442 -1.7045015 0.7962404 -1.696063 -1.4771117
## 4 -1.1780495 -2.9747574 -1.1471518 -1.2377013 -1.010672 -2.6055975
## 5 2.8813178 -1.8964081 -1.2653487 -1.7839754 -2.872581 2.3033464
## 6 0.4437973 0.6599325 -1.4029555 -2.3118258 -1.792232 1.3934380
## stat197 stat198 stat199 stat200 stat201 stat202
## 1 -0.83056986 0.9550526 -1.7025776 -2.8263099 -0.7023998 0.2272806
## 2 -1.42178249 -1.2471864 2.5723093 -0.0233496 -1.8975239 1.9472262
## 3 -0.19233958 -0.5161456 0.0279946 -1.2333704 -2.9672263 -2.8666208
## 4 -1.23145902 1.4728470 -0.4562025 -2.2983441 -1.5101184 0.2530525
## 5 1.85018563 -1.8269292 -0.6337969 -2.1473246 0.9909850 1.0950903
## 6 -0.09311061 0.5144456 -2.8178268 -2.7555969 -2.3546004 -1.0558939
## stat203 stat204 stat205 stat206 stat207 stat208
## 1 1.166631220 0.007453276 2.9961641 1.5327307 -2.2293356 -0.9946009
## 2 -0.235396504 2.132749800 0.3707606 1.5604026 -1.0089217 2.1474257
## 3 0.003180946 2.229793310 2.7354040 0.8992231 2.9694967 2.3081024
## 4 -0.474482715 -1.584772230 -2.3224132 -0.9409741 -2.3179255 0.8032548
## 5 2.349412920 -1.276320220 -2.0203719 -1.1733509 1.0371852 -2.5086207
## 6 0.727436960 -0.960191786 -0.8964998 -1.6406623 -0.2330488 1.7993879
## stat209 stat210 stat211 stat212 stat213 stat214
## 1 -2.2182105 -1.4099774 -1.656754 2.6602585 -2.9270992 1.1240714
## 2 -2.8932488 -1.1641679 -2.605423 -1.5650513 2.9523673 2.0266318
## 3 -1.8279589 0.0472350 -2.026734 2.5054367 0.9903042 0.3274105
## 4 -1.0878067 0.1171303 2.645891 -1.6775225 1.3452160 1.4694063
## 5 -0.8158175 0.4060950 0.912256 0.2925677 2.1610141 0.5679936
## 6 -2.2664354 -0.2061083 -1.435174 2.6645632 0.4216259 -0.6419122
## stat215 stat216 stat217
## 1 -2.7510750 -0.5501796 1.2638469
## 2 2.8934650 -2.4099574 -1.2411407
## 3 -1.0947676 1.2852937 1.5411530
## 4 0.6343777 0.1345372 2.9102673
## 5 0.9908702 1.7909757 -2.0902610
## 6 -2.8113887 -1.0624912 0.2765074features = features.highprec
#str(features) Checking correlations to evaluate removal of redundant features
corr.matrix = round(cor(features[sapply(features, is.numeric)]),2)
# filter out only highly correlated variables
threshold = 0.6
corr.matrix.tmp = corr.matrix
diag(corr.matrix.tmp) = 0
high.corr = apply(abs(corr.matrix.tmp) >= threshold, 1, any)
high.corr.matrix = corr.matrix.tmp[high.corr, high.corr]
DT::datatable(corr.matrix)DT::datatable(high.corr.matrix)Feature Names
feature.names = colnames(features)
drops <- c('JobName')
feature.names = feature.names[!(feature.names %in% drops)]
#str(feature.names)Read and Clean Labels
labels = read.csv("../../Data/labels.csv")
#str(labels)
labels = labels[,c("JobName", output.var)]
summary(labels)## JobName y3
## Job_00001: 1 Min. : 95.91
## Job_00002: 1 1st Qu.:118.21
## Job_00003: 1 Median :123.99
## Job_00004: 1 Mean :125.36
## Job_00005: 1 3rd Qu.:131.06
## Job_00006: 1 Max. :193.73
## (Other) :9994 NA's :2497Merge Datasets
data <- merge(features, labels, by = 'JobName')
drops <- c('JobName')
data = data[,(!colnames(data) %in% drops)]
#str(data)Transformations
if (transform.abs == TRUE){
data[,label.names] = 10^(data[,label.names]/20)
data = filter(data, y3 < 1E7)
}
#str(data)
if (log.pred == TRUE){
data[label.names] = log(data[alt.scale.label.name],10)
drops = c(alt.scale.label.name)
data = data[!(names(data) %in% drops)]
}
if (norm.pred){
t=bestNormalize::bestNormalize(data[[alt.scale.label.name]])
data[label.names] = predict(t)
drops = c(alt.scale.label.name)
data = data[!(names(data) %in% drops)]
}## Warning in orderNorm(standardize = TRUE, warn = TRUE, x = c(121.2556129, : Ties in data, Normal distribution not guaranteed#str(data)Remove NA Cases
data = data[complete.cases(data),]Exploratory Data Analysis
Check correlation of Label with Featires
if (eda == TRUE){
corr.to.label =round(cor(dplyr::select(data,-one_of(label.names)),dplyr::select_at(data,label.names)),4)
DT::datatable(corr.to.label)
}Multicollinearity - VIF
if (eda == TRUE){
vifDF = usdm::vif(select_at(data,feature.names)) %>% arrange(desc(VIF))
head(vifDF,10)
}Scatterplots
panel.hist <- function(x, ...)
{
usr <- par("usr"); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col = "cyan", ...)
}if (eda == TRUE){
histogram(data[ ,label.names])
#hist(data[complete.cases(data),alt.scale.label.name])
}# https://stackoverflow.com/questions/24648729/plot-one-numeric-variable-against-n-numeric-variables-in-n-plots
ind.pairs.plot <- function(data, xvars=NULL, yvar)
{
df <- data
if (is.null(xvars)) {
xvars = names(data[which(names(data)!=yvar)])
}
#choose a format to display charts
ncharts <- length(xvars)
for(i in 1:ncharts){
plot(df[,xvars[i]],df[,yvar], xlab = xvars[i], ylab = yvar)
}
}
if (eda == TRUE){
ind.pairs.plot(data, feature.names, label.names)
}
#
# pl <- ggplot(data, aes(x=x18, y = y3))
# pl2 <- pl + geom_point(aes(alpha = 0.1)) # default color gradient based on 'hp'
# print(pl2)Feature Engineering
if(eda ==FALSE){
# x18 may need transformations
plot(data[,'x18'], data[,label.names], main = "Original Scatter Plot vs. x18", ylab = label.names, xlab = 'x18')
plot(sqrt(data[,'x18']), data[,label.names], main = "Original Scatter Plot vs. sqrt(x18)", ylab = label.names, xlab = 'sqrt(x18)')
# transforming x18
data$sqrt.x18 = sqrt(data$x18)
data = dplyr::select(data,-one_of('x18'))
# what about x7, x9?
# x11 looks like data is at discrete points after a while. Will this be a problem?
}
Modeling
Train Test Split
data = data[sample(nrow(data)),] # randomly shuffle data
split = sample.split(data[,label.names], SplitRatio = 0.8)
data.train = subset(data, split == TRUE)
data.test = subset(data, split == FALSE)Common Functions
plot.diagnostics <- function(model, train) {
plot(model)
residuals = resid(model) # Plotted above in plot(lm.out)
r.standard = rstandard(model)
r.student = rstudent(model)
plot(predict(model,train),r.student,
ylab="Student Residuals", xlab="Predicted Values",
main="Student Residual Plot")
abline(0, 0)
plot(predict(model, train),r.standard,
ylab="Standard Residuals", xlab="Predicted Values",
main="Standard Residual Plot")
abline(0, 0)
abline(2, 0)
abline(-2, 0)
# Histogram
hist(r.student, freq=FALSE, main="Distribution of Studentized Residuals",
xlab="Studentized Residuals", ylab="Density", ylim=c(0,0.5))
# Create range of x-values for normal curve
xfit <- seq(min(r.student)-1, max(r.student)+1, length=40)
# Generate values from the normal distribution at the specified values
yfit <- (dnorm(xfit))
# Add the normal curve
lines(xfit, yfit, ylim=c(0,0.5))
# http://www.stat.columbia.edu/~martin/W2024/R7.pdf
# Influential plots
inf.meas = influence.measures(model)
# print (summary(inf.meas)) # too much data
# Leverage plot
lev = hat(model.matrix(model))
plot(lev, ylab = 'Leverage - check')
# Cook's Distance
cd = cooks.distance(model)
plot(cd,ylab="Cooks distances")
abline(4/nrow(train),0)
abline(1,0)
print (paste("Number of data points that have Cook's D > 4/n: ", length(cd[cd > 4/nrow(train)]), sep = ""))
print (paste("Number of data points that have Cook's D > 1: ", length(cd[cd > 1]), sep = ""))
return(cd)
}
train.caret.glmselect = function(formula, data, method
,subopt = NULL, feature.names
, train.control = NULL, tune.grid = NULL, pre.proc = NULL){
if(is.null(train.control)){
train.control <- trainControl(method = "cv"
,number = 10
,search = "grid"
,verboseIter = TRUE
,allowParallel = TRUE
)
}
if(is.null(tune.grid)){
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
tune.grid = data.frame(nvmax = 1:length(feature.names))
}
if (method == 'glmnet' && subopt == 'LASSO'){
# Will only show 1 Lambda value during training, but that is OK
# https://stackoverflow.com/questions/47526544/why-need-to-tune-lambda-with-carettrain-method-glmnet-and-cv-glmnet
# Another option for LASSO is this: https://github.com/topepo/caret/blob/master/RegressionTests/Code/lasso.R
lambda = 10^seq(-2,0, length =100)
alpha = c(1)
tune.grid = expand.grid(alpha = alpha,lambda = lambda)
}
if (method == 'lars'){
# https://github.com/topepo/caret/blob/master/RegressionTests/Code/lars.R
fraction = seq(0, 1, length = 100)
tune.grid = expand.grid(fraction = fraction)
pre.proc = c("center", "scale")
}
}
# http://sshaikh.org/2015/05/06/parallelize-machine-learning-in-r-with-multi-core-cpus/
cl <- makeCluster(detectCores()*0.75) # use 75% of cores only, leave rest for other tasks
registerDoParallel(cl)
set.seed(1)
# note that the seed has to actually be set just before this function is called
# settign is above just not ensure reproducibility for some reason
model.caret <- caret::train(formula
, data = data
, method = method
, tuneGrid = tune.grid
, trControl = train.control
, preProc = pre.proc
)
stopCluster(cl)
registerDoSEQ() # register sequential engine in case you are not using this function anymore
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
print(model.caret$results) # all model results
print(model.caret$bestTune) # best model
model = model.caret$finalModel
# Residuals Plot MMORO #
# leap function doens support studentized residuals
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
# Provides the coefficients of the best model
id = rownames(model.caret$bestTune)
message("Coefficients of final model:")
print (coef(model, id = id))
# Need to find alternate to plotting diagnostic plots
# plot.diagnostics(model.forward,data.train)
# plot(model.forward,labels = colnames(data.train),scale=c("bic")) ## too many variables
return(list(model = model,id = id,residPlot = residPlot ,residHistogram=residHistogram))
}
if (method == 'glmnet' && subopt == 'LASSO'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
# Metrics Plot MMORO#
dataPlot = cbind(model.caret$results, id=as.numeric(rownames(model.caret$results))) %>%
gather(key='metric',value='value',-id) %>%
dplyr::filter(metric %in% c('MAE','RMSE','Rsquared'))
metricsPlot = ggplot(data=dataPlot,aes(x=id,y=value) ) +
geom_line(color='lightblue4') +
geom_point(color='blue',alpha=0.7,size=.9) +
facet_wrap(~metric,ncol=4,scales='free_y')+
theme_light()
plot(metricsPlot)
# Residuals Plot MMORO#
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id,residPlot = residPlot,metricsPlot=metricsPlot ))
}
if (method == 'lars'){
print(model.caret)
print(plot(model.caret))
print(model.caret$bestTune)
# Residuals Plot MMORO#
dataPlot=data.frame(pred=predict(model.caret,data),res=resid(model.caret))
residPlot = ggplot(dataPlot,aes(x=pred,y=res)) +
geom_point(color='light blue',alpha=0.7) +
geom_smooth()+
theme_light()
plot(residPlot)
residHistogram = ggplot(dataPlot,aes(x=res)) +
geom_histogram(aes(y=..density..),fill='light blue',alpha=1) +
geom_density(color='lightblue4') +
theme_light()
plot(residHistogram)
id = NULL # not really needed but added for consistency
return(list(model = model.caret,id = id,residPlot = residPlot ,residHistogram=residHistogram))
}
}
# https://stackoverflow.com/questions/48265743/linear-model-subset-selection-goodness-of-fit-with-k-fold-cross-validation
# changes slightly since call[[2]] was just returning "formula" without actually returnign the value in formula
predict.regsubsets <- function(object, newdata, id, formula, ...) {
#form <- as.formula(object$call[[2]])
mat <- model.matrix(formula, newdata) # adds intercept and expands any interaction terms
coefi <- coef(object, id = id)
xvars <- names(coefi)
return(mat[,xvars]%*%coefi)
}
test.model = function(model, test, level=0.95
,draw.limits = FALSE, good = 0.1, ok = 0.15
,method = NULL, subopt = NULL
,id = NULL, formula, feature.names, label.names){
## if using caret for glm select equivalent functionality,
## need to set regsubset = TRUE, pass id of best model through id variable,
## and pass formula (full is ok as it will select subset of variables from there)
if (is.null(method)){
pred = predict(model, newdata=test, interval="confidence", level = level)
}
if (method == 'leapForward' | method == 'leapBackward' | method == 'leapSeq'){
pred = predict.regsubsets(model, newdata = test, id = id, formula = formula)
}
if (method == 'glmnet' && subopt == 'LASSO'){
xtest = as.matrix(test[,feature.names])
pred=as.data.frame(predict(model, xtest))
}
if (method == 'lars'){
pred=as.data.frame(predict(model, newdata = test))
}
# Summary of predicted values
print ("Summary of predicted values: ")
print(summary(pred[,1]))
test.mse = mean((test[,label.names]-pred[,1])^2)
print (paste(method, subopt, "Test MSE:", test.mse, sep=" "))
plot(test[,label.names],pred[,1],xlab = "Actual", ylab = "Predicted")
abline(0,(1+good),col='green', lwd = 3)
abline(0,(1-good),col='green', lwd = 3)
abline(0,(1+ok),col='blue', lwd = 3)
abline(0,(1-ok),col='blue', lwd = 3)
}Setup Formulae
n <- names(data.train)
formula <- as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~", paste(n[!n %in% label.names], collapse = " + ")))
grand.mean.formula = as.formula(paste(paste(n[n %in% label.names], collapse = " + ")," ~ 1"))
print(formula)## norm.y3 ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 +
## x11 + x12 + x13 + x14 + x15 + x16 + x17 + x19 + x20 + x21 +
## x22 + x23 + stat1 + stat2 + stat3 + stat4 + stat5 + stat6 +
## stat7 + stat8 + stat9 + stat10 + stat11 + stat12 + stat13 +
## stat14 + stat15 + stat16 + stat17 + stat18 + stat19 + stat20 +
## stat21 + stat22 + stat23 + stat24 + stat25 + stat26 + stat27 +
## stat28 + stat29 + stat30 + stat31 + stat32 + stat33 + stat34 +
## stat35 + stat36 + stat37 + stat38 + stat39 + stat40 + stat41 +
## stat42 + stat43 + stat44 + stat45 + stat46 + stat47 + stat48 +
## stat49 + stat50 + stat51 + stat52 + stat53 + stat54 + stat55 +
## stat56 + stat57 + stat58 + stat59 + stat60 + stat61 + stat62 +
## stat63 + stat64 + stat65 + stat66 + stat67 + stat68 + stat69 +
## stat70 + stat71 + stat72 + stat73 + stat74 + stat75 + stat76 +
## stat77 + stat78 + stat79 + stat80 + stat81 + stat82 + stat83 +
## stat84 + stat85 + stat86 + stat87 + stat88 + stat89 + stat90 +
## stat91 + stat92 + stat93 + stat94 + stat95 + stat96 + stat97 +
## stat98 + stat99 + stat100 + stat101 + stat102 + stat103 +
## stat104 + stat105 + stat106 + stat107 + stat108 + stat109 +
## stat110 + stat111 + stat112 + stat113 + stat114 + stat115 +
## stat116 + stat117 + stat118 + stat119 + stat120 + stat121 +
## stat122 + stat123 + stat124 + stat125 + stat126 + stat127 +
## stat128 + stat129 + stat130 + stat131 + stat132 + stat133 +
## stat134 + stat135 + stat136 + stat137 + stat138 + stat139 +
## stat140 + stat141 + stat142 + stat143 + stat144 + stat145 +
## stat146 + stat147 + stat148 + stat149 + stat150 + stat151 +
## stat152 + stat153 + stat154 + stat155 + stat156 + stat157 +
## stat158 + stat159 + stat160 + stat161 + stat162 + stat163 +
## stat164 + stat165 + stat166 + stat167 + stat168 + stat169 +
## stat170 + stat171 + stat172 + stat173 + stat174 + stat175 +
## stat176 + stat177 + stat178 + stat179 + stat180 + stat181 +
## stat182 + stat183 + stat184 + stat185 + stat186 + stat187 +
## stat188 + stat189 + stat190 + stat191 + stat192 + stat193 +
## stat194 + stat195 + stat196 + stat197 + stat198 + stat199 +
## stat200 + stat201 + stat202 + stat203 + stat204 + stat205 +
## stat206 + stat207 + stat208 + stat209 + stat210 + stat211 +
## stat212 + stat213 + stat214 + stat215 + stat216 + stat217 +
## sqrt.x18print(grand.mean.formula)## norm.y3 ~ 1# Update feature.names because we may have transformed some features
feature.names = n[!n %in% label.names]Full & Grand Means Model
model.full = lm(formula , data.train)
summary(model.full)##
## Call:
## lm(formula = formula, data = data.train)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.5824 -0.5814 -0.0776 0.5202 3.8650
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.925e+00 2.509e-01 -15.644 < 2e-16 ***
## x1 -8.424e-03 1.709e-02 -0.493 0.622160
## x2 1.604e-03 1.092e-02 0.147 0.883262
## x3 2.750e-03 2.991e-03 0.919 0.357971
## x4 -1.296e-03 2.359e-04 -5.495 4.07e-08 ***
## x5 8.787e-03 7.765e-03 1.132 0.257820
## x6 -3.845e-03 1.564e-02 -0.246 0.805832
## x7 3.575e-01 1.671e-02 21.393 < 2e-16 ***
## x8 1.221e-02 3.868e-03 3.156 0.001606 **
## x9 1.027e-01 8.660e-03 11.862 < 2e-16 ***
## x10 3.600e-02 8.080e-03 4.456 8.51e-06 ***
## x11 6.353e+06 1.935e+06 3.283 0.001033 **
## x12 -2.358e-03 4.886e-03 -0.483 0.629407
## x13 3.475e-03 1.970e-03 1.764 0.077761 .
## x14 -1.055e-02 8.454e-03 -1.248 0.211899
## x15 4.236e-03 8.068e-03 0.525 0.599540
## x16 2.648e-02 5.587e-03 4.739 2.20e-06 ***
## x17 3.891e-02 8.518e-03 4.568 5.03e-06 ***
## x19 6.818e-03 4.340e-03 1.571 0.116279
## x20 -2.677e-02 3.007e-02 -0.890 0.373278
## x21 3.683e-03 1.113e-03 3.310 0.000939 ***
## x22 -1.278e-02 9.037e-03 -1.414 0.157404
## x23 5.750e-03 8.567e-03 0.671 0.502136
## stat1 5.306e-04 6.498e-03 0.082 0.934913
## stat2 4.961e-03 6.431e-03 0.771 0.440480
## stat3 1.425e-02 6.508e-03 2.190 0.028584 *
## stat4 -1.443e-02 6.522e-03 -2.213 0.026933 *
## stat5 -4.770e-03 6.548e-03 -0.728 0.466415
## stat6 -2.974e-03 6.478e-03 -0.459 0.646212
## stat7 -7.498e-03 6.479e-03 -1.157 0.247200
## stat8 6.005e-03 6.471e-03 0.928 0.353476
## stat9 9.361e-05 6.469e-03 0.014 0.988455
## stat10 -6.550e-03 6.506e-03 -1.007 0.314061
## stat11 -8.064e-03 6.564e-03 -1.228 0.219350
## stat12 1.481e-03 6.498e-03 0.228 0.819741
## stat13 -8.888e-03 6.479e-03 -1.372 0.170157
## stat14 -3.082e-02 6.448e-03 -4.779 1.80e-06 ***
## stat15 -1.078e-02 6.453e-03 -1.670 0.094947 .
## stat16 -7.102e-04 6.485e-03 -0.110 0.912792
## stat17 -5.881e-04 6.441e-03 -0.091 0.927252
## stat18 -4.467e-03 6.448e-03 -0.693 0.488493
## stat19 3.606e-03 6.464e-03 0.558 0.576971
## stat20 -6.463e-03 6.465e-03 -1.000 0.317468
## stat21 -2.137e-04 6.499e-03 -0.033 0.973764
## stat22 -5.384e-03 6.490e-03 -0.830 0.406822
## stat23 1.421e-02 6.455e-03 2.201 0.027742 *
## stat24 -9.758e-03 6.494e-03 -1.503 0.132986
## stat25 -9.751e-03 6.477e-03 -1.505 0.132282
## stat26 -1.042e-02 6.469e-03 -1.611 0.107240
## stat27 5.590e-04 6.531e-03 0.086 0.931788
## stat28 6.646e-03 6.480e-03 1.026 0.305141
## stat29 4.429e-03 6.522e-03 0.679 0.497093
## stat30 5.488e-03 6.515e-03 0.842 0.399678
## stat31 -4.260e-03 6.512e-03 -0.654 0.513070
## stat32 1.588e-04 6.510e-03 0.024 0.980540
## stat33 -1.319e-02 6.463e-03 -2.040 0.041358 *
## stat34 3.860e-03 6.453e-03 0.598 0.549740
## stat35 -1.180e-02 6.509e-03 -1.813 0.069811 .
## stat36 5.037e-03 6.452e-03 0.781 0.435055
## stat37 -2.973e-03 6.536e-03 -0.455 0.649256
## stat38 1.348e-02 6.535e-03 2.063 0.039192 *
## stat39 -7.249e-03 6.440e-03 -1.126 0.260327
## stat40 -2.776e-03 6.463e-03 -0.430 0.667553
## stat41 -1.795e-02 6.437e-03 -2.789 0.005298 **
## stat42 -3.767e-03 6.487e-03 -0.581 0.561510
## stat43 -3.641e-03 6.477e-03 -0.562 0.574079
## stat44 4.024e-03 6.457e-03 0.623 0.533202
## stat45 -1.040e-02 6.468e-03 -1.608 0.107959
## stat46 9.085e-03 6.464e-03 1.405 0.159935
## stat47 2.750e-03 6.525e-03 0.422 0.673379
## stat48 1.249e-02 6.475e-03 1.929 0.053730 .
## stat49 5.456e-03 6.414e-03 0.851 0.395006
## stat50 4.527e-03 6.455e-03 0.701 0.483114
## stat51 1.378e-03 6.438e-03 0.214 0.830466
## stat52 1.929e-03 6.477e-03 0.298 0.765802
## stat53 -3.893e-03 6.523e-03 -0.597 0.550676
## stat54 -8.396e-03 6.512e-03 -1.289 0.197309
## stat55 5.636e-03 6.430e-03 0.876 0.380812
## stat56 -4.010e-03 6.535e-03 -0.614 0.539460
## stat57 5.938e-03 6.439e-03 0.922 0.356521
## stat58 2.603e-03 6.424e-03 0.405 0.685293
## stat59 4.149e-03 6.492e-03 0.639 0.522785
## stat60 1.136e-02 6.471e-03 1.756 0.079196 .
## stat61 -6.374e-03 6.497e-03 -0.981 0.326573
## stat62 -9.863e-03 6.457e-03 -1.527 0.126714
## stat63 5.283e-03 6.516e-03 0.811 0.417539
## stat64 -9.204e-04 6.455e-03 -0.143 0.886624
## stat65 -7.122e-03 6.497e-03 -1.096 0.273007
## stat66 6.816e-03 6.581e-03 1.036 0.300421
## stat67 1.034e-03 6.543e-03 0.158 0.874413
## stat68 -2.636e-03 6.524e-03 -0.404 0.686219
## stat69 -2.269e-03 6.479e-03 -0.350 0.726127
## stat70 6.023e-03 6.420e-03 0.938 0.348155
## stat71 3.863e-03 6.455e-03 0.598 0.549566
## stat72 3.487e-03 6.511e-03 0.536 0.592305
## stat73 8.544e-03 6.533e-03 1.308 0.191006
## stat74 -7.103e-03 6.489e-03 -1.095 0.273709
## stat75 -5.151e-03 6.515e-03 -0.791 0.429194
## stat76 4.510e-03 6.533e-03 0.690 0.490052
## stat77 -6.357e-03 6.472e-03 -0.982 0.326064
## stat78 -3.308e-03 6.478e-03 -0.511 0.609656
## stat79 -1.455e-03 6.471e-03 -0.225 0.822111
## stat80 7.024e-03 6.516e-03 1.078 0.281114
## stat81 6.587e-03 6.565e-03 1.003 0.315724
## stat82 1.606e-03 6.474e-03 0.248 0.804099
## stat83 -8.355e-03 6.471e-03 -1.291 0.196680
## stat84 -2.440e-04 6.510e-03 -0.037 0.970098
## stat85 -7.771e-03 6.466e-03 -1.202 0.229481
## stat86 -4.164e-04 6.494e-03 -0.064 0.948885
## stat87 -9.079e-03 6.523e-03 -1.392 0.164067
## stat88 -5.612e-03 6.447e-03 -0.870 0.384074
## stat89 -6.792e-03 6.447e-03 -1.053 0.292162
## stat90 -1.119e-02 6.489e-03 -1.725 0.084559 .
## stat91 -1.280e-02 6.417e-03 -1.994 0.046153 *
## stat92 -1.237e-02 6.504e-03 -1.901 0.057328 .
## stat93 -2.206e-03 6.536e-03 -0.338 0.735737
## stat94 -4.053e-03 6.484e-03 -0.625 0.531981
## stat95 -6.190e-04 6.486e-03 -0.095 0.923977
## stat96 -6.535e-03 6.463e-03 -1.011 0.311972
## stat97 -7.771e-04 6.450e-03 -0.120 0.904104
## stat98 1.008e-01 6.400e-03 15.745 < 2e-16 ***
## stat99 9.946e-03 6.518e-03 1.526 0.127055
## stat100 1.822e-02 6.512e-03 2.797 0.005172 **
## stat101 -5.475e-03 6.510e-03 -0.841 0.400314
## stat102 2.384e-03 6.514e-03 0.366 0.714359
## stat103 -1.011e-02 6.574e-03 -1.538 0.124173
## stat104 -3.356e-03 6.493e-03 -0.517 0.605242
## stat105 9.360e-03 6.409e-03 1.461 0.144179
## stat106 -7.519e-03 6.465e-03 -1.163 0.244821
## stat107 -2.934e-03 6.494e-03 -0.452 0.651389
## stat108 -9.204e-03 6.477e-03 -1.421 0.155327
## stat109 2.381e-03 6.480e-03 0.367 0.713312
## stat110 -9.748e-02 6.450e-03 -15.112 < 2e-16 ***
## stat111 8.153e-04 6.479e-03 0.126 0.899865
## stat112 -1.522e-03 6.510e-03 -0.234 0.815174
## stat113 -2.222e-03 6.533e-03 -0.340 0.733817
## stat114 -1.945e-03 6.484e-03 -0.300 0.764223
## stat115 1.023e-03 6.458e-03 0.158 0.874110
## stat116 1.614e-03 6.504e-03 0.248 0.804026
## stat117 3.608e-03 6.476e-03 0.557 0.577495
## stat118 -3.648e-03 6.442e-03 -0.566 0.571298
## stat119 4.460e-03 6.519e-03 0.684 0.493931
## stat120 2.716e-03 6.419e-03 0.423 0.672243
## stat121 -5.138e-03 6.486e-03 -0.792 0.428309
## stat122 1.026e-03 6.448e-03 0.159 0.873608
## stat123 -2.169e-03 6.577e-03 -0.330 0.741575
## stat124 -3.409e-03 6.457e-03 -0.528 0.597535
## stat125 1.659e-03 6.501e-03 0.255 0.798525
## stat126 6.894e-03 6.467e-03 1.066 0.286460
## stat127 -2.753e-04 6.459e-03 -0.043 0.966005
## stat128 -4.108e-03 6.472e-03 -0.635 0.525641
## stat129 2.687e-03 6.475e-03 0.415 0.678167
## stat130 1.443e-03 6.506e-03 0.222 0.824457
## stat131 -1.149e-03 6.498e-03 -0.177 0.859643
## stat132 -6.792e-03 6.484e-03 -1.047 0.294955
## stat133 4.523e-03 6.510e-03 0.695 0.487259
## stat134 -1.086e-02 6.438e-03 -1.688 0.091548 .
## stat135 1.683e-03 6.482e-03 0.260 0.795169
## stat136 6.884e-03 6.516e-03 1.056 0.290808
## stat137 -4.061e-03 6.425e-03 -0.632 0.527400
## stat138 -7.573e-04 6.470e-03 -0.117 0.906837
## stat139 5.477e-03 6.509e-03 0.841 0.400123
## stat140 3.217e-03 6.467e-03 0.497 0.618950
## stat141 5.582e-03 6.461e-03 0.864 0.387659
## stat142 -3.831e-03 6.523e-03 -0.587 0.557071
## stat143 7.471e-04 6.481e-03 0.115 0.908230
## stat144 1.057e-02 6.461e-03 1.636 0.101865
## stat145 -3.591e-03 6.540e-03 -0.549 0.583016
## stat146 -1.198e-02 6.496e-03 -1.845 0.065088 .
## stat147 -1.087e-02 6.579e-03 -1.653 0.098378 .
## stat148 -5.762e-03 6.410e-03 -0.899 0.368767
## stat149 -1.406e-02 6.555e-03 -2.145 0.032034 *
## stat150 1.900e-03 6.503e-03 0.292 0.770215
## stat151 -9.335e-03 6.566e-03 -1.422 0.155156
## stat152 -5.659e-03 6.473e-03 -0.874 0.382049
## stat153 4.693e-03 6.578e-03 0.713 0.475618
## stat154 4.456e-04 6.544e-03 0.068 0.945715
## stat155 -3.110e-03 6.475e-03 -0.480 0.631050
## stat156 1.176e-02 6.469e-03 1.818 0.069102 .
## stat157 4.279e-03 6.447e-03 0.664 0.506869
## stat158 -4.047e-03 6.592e-03 -0.614 0.539295
## stat159 5.362e-04 6.452e-03 0.083 0.933769
## stat160 7.874e-04 6.546e-03 0.120 0.904267
## stat161 8.361e-03 6.520e-03 1.282 0.199796
## stat162 2.024e-04 6.442e-03 0.031 0.974938
## stat163 5.648e-03 6.531e-03 0.865 0.387177
## stat164 1.186e-02 6.539e-03 1.814 0.069757 .
## stat165 -1.501e-03 6.472e-03 -0.232 0.816644
## stat166 -1.019e-02 6.421e-03 -1.588 0.112437
## stat167 -8.326e-03 6.475e-03 -1.286 0.198549
## stat168 -2.159e-03 6.472e-03 -0.334 0.738672
## stat169 7.502e-05 6.510e-03 0.012 0.990807
## stat170 -1.537e-03 6.509e-03 -0.236 0.813387
## stat171 5.019e-03 6.538e-03 0.768 0.442701
## stat172 7.204e-03 6.475e-03 1.113 0.265966
## stat173 -2.399e-03 6.498e-03 -0.369 0.711956
## stat174 -3.187e-03 6.511e-03 -0.489 0.624537
## stat175 -3.992e-03 6.509e-03 -0.613 0.539701
## stat176 1.801e-03 6.466e-03 0.279 0.780605
## stat177 -4.816e-03 6.498e-03 -0.741 0.458662
## stat178 -5.633e-03 6.515e-03 -0.865 0.387320
## stat179 9.150e-04 6.491e-03 0.141 0.887900
## stat180 -4.688e-03 6.432e-03 -0.729 0.466122
## stat181 4.089e-03 6.494e-03 0.630 0.528909
## stat182 2.376e-03 6.547e-03 0.363 0.716690
## stat183 8.397e-03 6.453e-03 1.301 0.193234
## stat184 1.135e-04 6.524e-03 0.017 0.986121
## stat185 -5.280e-04 6.444e-03 -0.082 0.934705
## stat186 1.714e-03 6.522e-03 0.263 0.792727
## stat187 -1.221e-02 6.451e-03 -1.893 0.058448 .
## stat188 -5.587e-03 6.463e-03 -0.864 0.387373
## stat189 2.097e-03 6.495e-03 0.323 0.746862
## stat190 -2.773e-03 6.456e-03 -0.429 0.667589
## stat191 -8.789e-03 6.500e-03 -1.352 0.176367
## stat192 1.593e-03 6.555e-03 0.243 0.808032
## stat193 -7.033e-04 6.545e-03 -0.107 0.914436
## stat194 4.268e-04 6.475e-03 0.066 0.947447
## stat195 1.011e-02 6.485e-03 1.560 0.118920
## stat196 -2.457e-03 6.568e-03 -0.374 0.708412
## stat197 6.726e-03 6.431e-03 1.046 0.295651
## stat198 -1.275e-02 6.478e-03 -1.968 0.049123 *
## stat199 6.429e-03 6.446e-03 0.997 0.318574
## stat200 -5.881e-03 6.411e-03 -0.917 0.358979
## stat201 -4.094e-03 6.457e-03 -0.634 0.526075
## stat202 -1.800e-03 6.559e-03 -0.274 0.783768
## stat203 4.578e-03 6.501e-03 0.704 0.481387
## stat204 -1.071e-02 6.469e-03 -1.656 0.097739 .
## stat205 -7.346e-03 6.464e-03 -1.137 0.255774
## stat206 -6.028e-03 6.520e-03 -0.925 0.355220
## stat207 6.624e-03 6.494e-03 1.020 0.307799
## stat208 1.594e-03 6.484e-03 0.246 0.805759
## stat209 -2.027e-03 6.454e-03 -0.314 0.753465
## stat210 -5.345e-03 6.527e-03 -0.819 0.412879
## stat211 -3.052e-03 6.451e-03 -0.473 0.636210
## stat212 2.406e-03 6.515e-03 0.369 0.711924
## stat213 -3.216e-03 6.520e-03 -0.493 0.621805
## stat214 -1.220e-02 6.458e-03 -1.888 0.059012 .
## stat215 -6.541e-03 6.483e-03 -1.009 0.312996
## stat216 1.393e-03 6.488e-03 0.215 0.829983
## stat217 1.054e-02 6.498e-03 1.622 0.104903
## sqrt.x18 7.743e-01 2.478e-02 31.255 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8528 on 5761 degrees of freedom
## Multiple R-squared: 0.3018, Adjusted R-squared: 0.2727
## F-statistic: 10.38 on 240 and 5761 DF, p-value: < 2.2e-16cd.full = plot.diagnostics(model.full, data.train)
## [1] "Number of data points that have Cook's D > 4/n: 308"
## [1] "Number of data points that have Cook's D > 1: 0"Checking with removal of high influence points
high.cd = names(cd.full[cd.full > 4/nrow(data.train)])
data.train2 = data.train[!(rownames(data.train)) %in% high.cd,]
model.full2 = lm(formula , data.train2)
summary(model.full2)##
## Call:
## lm(formula = formula, data = data.train2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.69772 -0.51462 -0.04202 0.50328 1.90918
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.205e+00 2.193e-01 -19.178 < 2e-16 ***
## x1 -1.668e-02 1.495e-02 -1.116 0.264501
## x2 4.813e-03 9.526e-03 0.505 0.613359
## x3 2.646e-03 2.607e-03 1.015 0.310017
## x4 -1.571e-03 2.062e-04 -7.617 3.03e-14 ***
## x5 1.597e-02 6.776e-03 2.357 0.018462 *
## x6 -7.895e-03 1.364e-02 -0.579 0.562733
## x7 3.857e-01 1.459e-02 26.428 < 2e-16 ***
## x8 1.178e-02 3.382e-03 3.484 0.000498 ***
## x9 1.040e-01 7.548e-03 13.775 < 2e-16 ***
## x10 4.481e-02 7.069e-03 6.339 2.50e-10 ***
## x11 6.478e+06 1.690e+06 3.834 0.000128 ***
## x12 7.822e-04 4.246e-03 0.184 0.853866
## x13 4.498e-03 1.719e-03 2.616 0.008922 **
## x14 -1.284e-02 7.379e-03 -1.740 0.081955 .
## x15 2.730e-03 7.044e-03 0.388 0.698396
## x16 2.664e-02 4.882e-03 5.457 5.07e-08 ***
## x17 4.233e-02 7.440e-03 5.689 1.34e-08 ***
## x19 8.100e-03 3.794e-03 2.135 0.032832 *
## x20 -1.754e-02 2.626e-02 -0.668 0.504131
## x21 4.327e-03 9.703e-04 4.460 8.36e-06 ***
## x22 -1.846e-02 7.873e-03 -2.344 0.019089 *
## x23 7.219e-03 7.489e-03 0.964 0.335146
## stat1 7.278e-04 5.666e-03 0.128 0.897798
## stat2 5.034e-03 5.606e-03 0.898 0.369282
## stat3 1.545e-02 5.684e-03 2.718 0.006589 **
## stat4 -1.643e-02 5.697e-03 -2.885 0.003935 **
## stat5 -9.143e-03 5.726e-03 -1.597 0.110389
## stat6 -4.596e-03 5.646e-03 -0.814 0.415670
## stat7 -1.053e-02 5.635e-03 -1.869 0.061653 .
## stat8 5.790e-03 5.647e-03 1.025 0.305230
## stat9 -3.224e-04 5.653e-03 -0.057 0.954525
## stat10 -4.072e-03 5.661e-03 -0.719 0.471957
## stat11 -1.190e-02 5.724e-03 -2.080 0.037618 *
## stat12 1.802e-03 5.671e-03 0.318 0.750644
## stat13 -7.549e-03 5.659e-03 -1.334 0.182322
## stat14 -3.528e-02 5.619e-03 -6.279 3.66e-10 ***
## stat15 -1.604e-02 5.643e-03 -2.843 0.004486 **
## stat16 -3.426e-03 5.656e-03 -0.606 0.544722
## stat17 -1.397e-04 5.625e-03 -0.025 0.980192
## stat18 -5.853e-03 5.618e-03 -1.042 0.297485
## stat19 7.571e-04 5.657e-03 0.134 0.893536
## stat20 4.142e-04 5.647e-03 0.073 0.941533
## stat21 2.304e-03 5.668e-03 0.406 0.684445
## stat22 -1.999e-03 5.665e-03 -0.353 0.724252
## stat23 1.172e-02 5.640e-03 2.078 0.037764 *
## stat24 -1.004e-02 5.665e-03 -1.771 0.076570 .
## stat25 -1.043e-02 5.652e-03 -1.845 0.065126 .
## stat26 -1.255e-02 5.652e-03 -2.220 0.026484 *
## stat27 1.580e-03 5.719e-03 0.276 0.782358
## stat28 2.182e-03 5.661e-03 0.385 0.699969
## stat29 6.051e-03 5.688e-03 1.064 0.287433
## stat30 4.618e-03 5.676e-03 0.814 0.415911
## stat31 -3.400e-03 5.680e-03 -0.599 0.549484
## stat32 1.068e-03 5.697e-03 0.187 0.851313
## stat33 -1.224e-02 5.647e-03 -2.167 0.030313 *
## stat34 7.613e-03 5.638e-03 1.350 0.176947
## stat35 -1.314e-02 5.676e-03 -2.315 0.020647 *
## stat36 6.747e-03 5.643e-03 1.196 0.231909
## stat37 -6.245e-03 5.701e-03 -1.095 0.273392
## stat38 1.613e-02 5.689e-03 2.836 0.004587 **
## stat39 -1.145e-02 5.604e-03 -2.043 0.041061 *
## stat40 4.208e-04 5.645e-03 0.075 0.940583
## stat41 -1.741e-02 5.618e-03 -3.099 0.001954 **
## stat42 -3.155e-03 5.669e-03 -0.556 0.577910
## stat43 -4.673e-03 5.644e-03 -0.828 0.407692
## stat44 6.983e-03 5.642e-03 1.238 0.215884
## stat45 -8.337e-03 5.638e-03 -1.479 0.139299
## stat46 4.736e-03 5.631e-03 0.841 0.400398
## stat47 6.787e-03 5.695e-03 1.192 0.233403
## stat48 1.034e-02 5.635e-03 1.835 0.066516 .
## stat49 -4.763e-04 5.603e-03 -0.085 0.932247
## stat50 1.481e-04 5.630e-03 0.026 0.979006
## stat51 9.248e-04 5.623e-03 0.164 0.869375
## stat52 7.163e-03 5.658e-03 1.266 0.205536
## stat53 -2.502e-04 5.688e-03 -0.044 0.964916
## stat54 -8.725e-03 5.686e-03 -1.534 0.125011
## stat55 3.812e-03 5.601e-03 0.681 0.496181
## stat56 -1.085e-03 5.701e-03 -0.190 0.849106
## stat57 6.893e-03 5.629e-03 1.225 0.220787
## stat58 2.476e-03 5.595e-03 0.442 0.658200
## stat59 3.223e-03 5.655e-03 0.570 0.568781
## stat60 1.471e-02 5.655e-03 2.601 0.009331 **
## stat61 -7.497e-03 5.669e-03 -1.322 0.186068
## stat62 -1.266e-02 5.632e-03 -2.248 0.024642 *
## stat63 6.482e-04 5.687e-03 0.114 0.909261
## stat64 2.913e-03 5.633e-03 0.517 0.605053
## stat65 -1.790e-03 5.678e-03 -0.315 0.752554
## stat66 5.189e-03 5.743e-03 0.904 0.366203
## stat67 5.909e-03 5.710e-03 1.035 0.300783
## stat68 -5.228e-03 5.705e-03 -0.916 0.359457
## stat69 -1.852e-03 5.668e-03 -0.327 0.743902
## stat70 8.608e-03 5.601e-03 1.537 0.124372
## stat71 5.698e-03 5.644e-03 1.010 0.312738
## stat72 -4.614e-03 5.677e-03 -0.813 0.416475
## stat73 8.586e-03 5.704e-03 1.505 0.132319
## stat74 -5.344e-03 5.665e-03 -0.943 0.345513
## stat75 -1.173e-03 5.681e-03 -0.206 0.836454
## stat76 4.965e-03 5.694e-03 0.872 0.383269
## stat77 -1.238e-03 5.655e-03 -0.219 0.826778
## stat78 -9.090e-03 5.639e-03 -1.612 0.107001
## stat79 9.345e-04 5.640e-03 0.166 0.868405
## stat80 8.065e-03 5.694e-03 1.416 0.156758
## stat81 2.785e-03 5.731e-03 0.486 0.627043
## stat82 -1.287e-03 5.649e-03 -0.228 0.819845
## stat83 -7.553e-03 5.647e-03 -1.337 0.181134
## stat84 -2.943e-03 5.679e-03 -0.518 0.604343
## stat85 -1.530e-02 5.642e-03 -2.713 0.006695 **
## stat86 2.250e-03 5.672e-03 0.397 0.691604
## stat87 -6.908e-03 5.690e-03 -1.214 0.224742
## stat88 2.217e-04 5.628e-03 0.039 0.968575
## stat89 -3.752e-03 5.634e-03 -0.666 0.505486
## stat90 -1.305e-02 5.661e-03 -2.306 0.021161 *
## stat91 -1.320e-02 5.592e-03 -2.361 0.018250 *
## stat92 -9.678e-03 5.671e-03 -1.707 0.087958 .
## stat93 -3.247e-03 5.722e-03 -0.567 0.570422
## stat94 -1.818e-04 5.651e-03 -0.032 0.974341
## stat95 1.936e-03 5.658e-03 0.342 0.732261
## stat96 -7.451e-03 5.632e-03 -1.323 0.185901
## stat97 1.082e-03 5.632e-03 0.192 0.847598
## stat98 1.073e-01 5.586e-03 19.205 < 2e-16 ***
## stat99 1.233e-02 5.685e-03 2.169 0.030132 *
## stat100 2.196e-02 5.670e-03 3.873 0.000109 ***
## stat101 -4.710e-03 5.675e-03 -0.830 0.406584
## stat102 2.789e-03 5.674e-03 0.492 0.623063
## stat103 -1.390e-02 5.726e-03 -2.428 0.015198 *
## stat104 -3.199e-03 5.680e-03 -0.563 0.573374
## stat105 9.378e-03 5.599e-03 1.675 0.094014 .
## stat106 -6.985e-03 5.654e-03 -1.235 0.216743
## stat107 -4.511e-04 5.659e-03 -0.080 0.936476
## stat108 -1.087e-02 5.662e-03 -1.921 0.054832 .
## stat109 -1.123e-03 5.659e-03 -0.198 0.842762
## stat110 -1.020e-01 5.632e-03 -18.115 < 2e-16 ***
## stat111 -4.601e-04 5.648e-03 -0.081 0.935077
## stat112 2.455e-03 5.676e-03 0.433 0.665348
## stat113 -9.441e-04 5.704e-03 -0.166 0.868550
## stat114 3.618e-04 5.653e-03 0.064 0.948976
## stat115 2.599e-03 5.638e-03 0.461 0.644844
## stat116 6.369e-04 5.682e-03 0.112 0.910766
## stat117 8.288e-03 5.641e-03 1.469 0.141849
## stat118 2.390e-03 5.621e-03 0.425 0.670748
## stat119 8.543e-03 5.687e-03 1.502 0.133124
## stat120 -2.313e-03 5.593e-03 -0.414 0.679184
## stat121 -5.616e-03 5.659e-03 -0.992 0.321046
## stat122 -3.784e-03 5.638e-03 -0.671 0.502199
## stat123 3.790e-03 5.743e-03 0.660 0.509376
## stat124 -5.722e-03 5.639e-03 -1.015 0.310289
## stat125 -1.847e-03 5.680e-03 -0.325 0.745082
## stat126 7.792e-03 5.648e-03 1.380 0.167751
## stat127 -2.125e-03 5.637e-03 -0.377 0.706198
## stat128 -6.638e-03 5.637e-03 -1.178 0.239009
## stat129 -7.598e-04 5.642e-03 -0.135 0.892893
## stat130 4.827e-04 5.675e-03 0.085 0.932220
## stat131 1.364e-03 5.665e-03 0.241 0.809774
## stat132 -9.166e-03 5.659e-03 -1.620 0.105353
## stat133 7.183e-03 5.707e-03 1.259 0.208240
## stat134 -1.244e-02 5.621e-03 -2.212 0.026994 *
## stat135 -1.738e-04 5.658e-03 -0.031 0.975497
## stat136 -9.758e-04 5.680e-03 -0.172 0.863609
## stat137 -1.223e-04 5.595e-03 -0.022 0.982562
## stat138 1.082e-03 5.653e-03 0.191 0.848213
## stat139 2.049e-03 5.676e-03 0.361 0.718157
## stat140 3.304e-03 5.625e-03 0.587 0.557008
## stat141 7.381e-03 5.629e-03 1.311 0.189836
## stat142 -1.495e-03 5.683e-03 -0.263 0.792472
## stat143 -2.851e-03 5.664e-03 -0.503 0.614681
## stat144 9.254e-03 5.650e-03 1.638 0.101539
## stat145 -2.484e-03 5.715e-03 -0.435 0.663901
## stat146 -1.015e-02 5.666e-03 -1.791 0.073374 .
## stat147 -1.572e-02 5.744e-03 -2.736 0.006232 **
## stat148 -9.445e-03 5.607e-03 -1.685 0.092121 .
## stat149 -1.639e-02 5.734e-03 -2.859 0.004268 **
## stat150 -3.435e-03 5.684e-03 -0.604 0.545614
## stat151 -5.604e-03 5.751e-03 -0.974 0.329897
## stat152 -2.266e-03 5.644e-03 -0.401 0.688148
## stat153 6.645e-03 5.728e-03 1.160 0.246040
## stat154 -3.118e-04 5.713e-03 -0.055 0.956483
## stat155 4.849e-03 5.663e-03 0.856 0.391945
## stat156 8.893e-03 5.643e-03 1.576 0.115107
## stat157 5.961e-03 5.621e-03 1.060 0.288975
## stat158 2.188e-03 5.759e-03 0.380 0.704033
## stat159 4.803e-03 5.632e-03 0.853 0.393830
## stat160 -2.116e-03 5.720e-03 -0.370 0.711374
## stat161 6.496e-03 5.688e-03 1.142 0.253552
## stat162 -3.312e-04 5.609e-03 -0.059 0.952917
## stat163 5.626e-03 5.708e-03 0.986 0.324283
## stat164 7.250e-03 5.704e-03 1.271 0.203752
## stat165 -4.282e-03 5.641e-03 -0.759 0.447869
## stat166 -9.368e-03 5.590e-03 -1.676 0.093820 .
## stat167 -1.262e-02 5.653e-03 -2.233 0.025612 *
## stat168 -1.203e-03 5.640e-03 -0.213 0.831134
## stat169 4.260e-03 5.698e-03 0.748 0.454707
## stat170 -3.101e-03 5.690e-03 -0.545 0.585786
## stat171 4.863e-04 5.703e-03 0.085 0.932052
## stat172 9.813e-03 5.637e-03 1.741 0.081779 .
## stat173 4.704e-03 5.671e-03 0.830 0.406841
## stat174 -7.328e-04 5.676e-03 -0.129 0.897275
## stat175 -5.938e-03 5.672e-03 -1.047 0.295183
## stat176 -4.896e-03 5.632e-03 -0.869 0.384699
## stat177 -1.213e-02 5.666e-03 -2.140 0.032381 *
## stat178 -1.893e-03 5.685e-03 -0.333 0.739188
## stat179 3.297e-03 5.681e-03 0.580 0.561708
## stat180 -6.717e-03 5.627e-03 -1.194 0.232671
## stat181 4.046e-03 5.663e-03 0.714 0.474981
## stat182 3.202e-03 5.727e-03 0.559 0.576109
## stat183 9.892e-03 5.639e-03 1.754 0.079448 .
## stat184 2.195e-03 5.692e-03 0.386 0.699864
## stat185 2.219e-03 5.639e-03 0.393 0.693985
## stat186 5.739e-03 5.689e-03 1.009 0.313170
## stat187 -9.522e-03 5.624e-03 -1.693 0.090493 .
## stat188 -2.527e-03 5.641e-03 -0.448 0.654159
## stat189 -3.673e-03 5.673e-03 -0.647 0.517408
## stat190 -1.893e-03 5.632e-03 -0.336 0.736851
## stat191 -9.444e-03 5.661e-03 -1.668 0.095353 .
## stat192 6.100e-04 5.720e-03 0.107 0.915086
## stat193 7.660e-03 5.716e-03 1.340 0.180264
## stat194 -1.229e-04 5.664e-03 -0.022 0.982685
## stat195 1.106e-02 5.676e-03 1.949 0.051304 .
## stat196 -6.343e-03 5.731e-03 -1.107 0.268454
## stat197 2.048e-03 5.615e-03 0.365 0.715356
## stat198 -1.603e-02 5.652e-03 -2.836 0.004591 **
## stat199 2.513e-03 5.632e-03 0.446 0.655502
## stat200 -2.667e-03 5.612e-03 -0.475 0.634656
## stat201 -9.926e-05 5.641e-03 -0.018 0.985961
## stat202 4.624e-04 5.720e-03 0.081 0.935576
## stat203 4.582e-03 5.678e-03 0.807 0.419790
## stat204 -6.742e-03 5.654e-03 -1.193 0.233104
## stat205 -1.238e-03 5.633e-03 -0.220 0.826012
## stat206 -9.062e-03 5.683e-03 -1.594 0.110902
## stat207 1.366e-02 5.670e-03 2.410 0.015993 *
## stat208 3.377e-03 5.670e-03 0.596 0.551504
## stat209 1.337e-03 5.619e-03 0.238 0.811968
## stat210 -8.234e-03 5.692e-03 -1.447 0.148057
## stat211 -4.407e-03 5.638e-03 -0.782 0.434512
## stat212 6.703e-03 5.689e-03 1.178 0.238749
## stat213 -2.713e-03 5.681e-03 -0.477 0.633049
## stat214 -7.100e-03 5.650e-03 -1.257 0.208899
## stat215 -7.214e-03 5.656e-03 -1.276 0.202168
## stat216 3.714e-03 5.661e-03 0.656 0.511795
## stat217 6.777e-03 5.668e-03 1.196 0.231934
## sqrt.x18 8.021e-01 2.161e-02 37.116 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.724 on 5453 degrees of freedom
## Multiple R-squared: 0.4011, Adjusted R-squared: 0.3748
## F-statistic: 15.22 on 240 and 5453 DF, p-value: < 2.2e-16cd.full2 = plot.diagnostics(model.full2, data.train2)
## [1] "Number of data points that have Cook's D > 4/n: 251"
## [1] "Number of data points that have Cook's D > 1: 0"# much more normal residuals than before.
# See if you can check the distribution (boxplots) of the high leverage points and the other points
# High Leverage Plot MMORO ###
plotData = data.train %>%
rownames_to_column() %>%
mutate(type=ifelse(rowname %in% high.cd,'High','Normal')) %>%
dplyr::select(type,target=one_of(label.names))
ggplot(data=plotData, aes(x=type,y=target)) +
geom_boxplot(fill='light blue',outlier.shape=NA) +
scale_y_continuous(name="Target Variable Values") +
theme_light() +
ggtitle('Distribution of High Leverage Points and Normal Points')
model.null = lm(grand.mean.formula, data.train)
# summary(model.null)
# plot.diagnostics(model.null, data.train)model.null2 = lm(grand.mean.formula, data.train2)
# summary(model.null2)
# plot.diagnostics(model.null2, data.train2)Variable Selection
Basic: http://www.stat.columbia.edu/~martin/W2024/R10.pdf Cross Validation + Other Metrics: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/154-stepwise-regression-essentials-in-r/
Forward Selection (w/ full train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward = step(model.null, scope=list(lower=model.null, upper=model.full), direction="forward", trace = 0)
print(summary(model.forward))
#saveRDS(model.forward,file = "model_forward.rds")
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward, data.train)
}Test
if (algo.forward == TRUE){
test.model(model.forward, data.test, "Forward Selection")
}Forward Selection (w/ filtered train)
Train
if (algo.forward == TRUE){
t1 = Sys.time()
model.forward2 = step(model.null2, scope=list(lower=model.null2, upper=model.full2), direction="forward", trace = 0)
print(summary(model.forward2))
#saveRDS(model.forward,file = "model_forward.rds")
t2 = Sys.time()
print (paste("Time taken for Forward Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.forward2, data.train2)
}Test
if (algo.forward == TRUE){
test.model(model.forward2, data.test, "Forward Selection (2)")
}Forward Selection with CV (w/ full train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
, data = data.train
, method = "leapForward"
, feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 13 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.9365643 0.1236737 0.7536160 0.01668298 0.01993896 0.01279423
## 2 2 0.9079494 0.1770387 0.7298664 0.01750665 0.02892992 0.01357292
## 3 3 0.8944193 0.2008249 0.7155952 0.01901631 0.02919065 0.01320105
## 4 4 0.8742691 0.2363913 0.6937420 0.02185141 0.03097169 0.01484363
## 5 5 0.8638186 0.2544505 0.6856338 0.02338283 0.03597771 0.01555964
## 6 6 0.8614669 0.2584423 0.6838031 0.02330853 0.03655409 0.01630994
## 7 7 0.8616264 0.2581201 0.6838827 0.02252992 0.03576400 0.01543925
## 8 8 0.8606372 0.2598909 0.6832436 0.02346111 0.03617819 0.01574605
## 9 9 0.8589201 0.2629713 0.6817973 0.02303138 0.03595457 0.01457108
## 10 10 0.8560569 0.2677599 0.6802123 0.02220196 0.03458445 0.01366388
## 11 11 0.8566254 0.2668271 0.6804815 0.02260781 0.03462453 0.01470736
## 12 12 0.8560935 0.2677508 0.6801040 0.02254458 0.03351414 0.01464169
## 13 13 0.8557582 0.2683145 0.6801978 0.02209939 0.03340193 0.01515055
## 14 14 0.8560136 0.2678391 0.6807736 0.02126001 0.03146312 0.01439859
## 15 15 0.8564294 0.2671722 0.6809500 0.02114252 0.03193376 0.01400192
## 16 16 0.8572779 0.2657165 0.6813558 0.02112224 0.03148819 0.01412545
## 17 17 0.8584022 0.2638391 0.6812557 0.02118983 0.03089284 0.01417726
## 18 18 0.8584713 0.2637941 0.6812881 0.02175667 0.03231409 0.01475928
## 19 19 0.8586138 0.2635734 0.6814524 0.02161393 0.03117075 0.01456148
## 20 20 0.8592247 0.2625221 0.6818734 0.02162926 0.03098543 0.01479863
## 21 21 0.8591232 0.2627095 0.6817160 0.02100173 0.03027013 0.01418736
## 22 22 0.8595672 0.2619685 0.6819990 0.02036708 0.02970497 0.01366103
## 23 23 0.8599657 0.2613521 0.6822373 0.01991861 0.02926440 0.01327024
## 24 24 0.8600881 0.2611761 0.6823737 0.02007091 0.02934298 0.01373595
## 25 25 0.8597106 0.2618264 0.6820676 0.01977088 0.02939082 0.01361317
## 26 26 0.8597122 0.2618222 0.6821121 0.01991373 0.02947959 0.01351560
## 27 27 0.8602658 0.2609066 0.6828477 0.01955309 0.02872751 0.01344418
## 28 28 0.8607311 0.2601434 0.6837223 0.01927953 0.02851973 0.01333752
## 29 29 0.8612540 0.2593429 0.6842532 0.01932870 0.02832327 0.01312468
## 30 30 0.8612463 0.2593275 0.6845514 0.01892673 0.02811348 0.01318904
## 31 31 0.8611470 0.2595574 0.6841864 0.01944845 0.02890544 0.01335162
## 32 32 0.8615212 0.2588937 0.6840893 0.01914864 0.02839104 0.01349669
## 33 33 0.8617137 0.2585664 0.6841523 0.01934134 0.02923018 0.01385934
## 34 34 0.8618148 0.2584260 0.6839470 0.01912075 0.02953677 0.01375559
## 35 35 0.8622505 0.2577406 0.6843609 0.01905131 0.02905255 0.01360498
## 36 36 0.8627138 0.2569905 0.6846949 0.01920017 0.02907708 0.01353399
## 37 37 0.8627289 0.2569871 0.6847022 0.01946511 0.02971603 0.01372192
## 38 38 0.8624466 0.2574493 0.6843393 0.01939203 0.02970203 0.01359252
## 39 39 0.8623031 0.2577069 0.6840452 0.01954666 0.03006506 0.01374611
## 40 40 0.8624198 0.2574951 0.6841652 0.01921446 0.02954165 0.01378647
## 41 41 0.8626752 0.2571075 0.6841373 0.01945054 0.02956580 0.01396677
## 42 42 0.8629127 0.2567708 0.6843409 0.01982525 0.03012414 0.01429586
## 43 43 0.8627881 0.2569998 0.6840421 0.02016666 0.03039427 0.01463079
## 44 44 0.8631789 0.2563902 0.6843141 0.01985587 0.03001733 0.01443269
## 45 45 0.8633226 0.2561757 0.6842709 0.01993758 0.02977203 0.01446995
## 46 46 0.8635918 0.2557670 0.6843090 0.01949306 0.02977477 0.01394600
## 47 47 0.8638782 0.2552995 0.6846696 0.01951231 0.02909490 0.01415426
## 48 48 0.8639558 0.2552146 0.6848365 0.01960761 0.02876838 0.01391325
## 49 49 0.8642093 0.2548113 0.6850011 0.01962428 0.02901819 0.01392481
## 50 50 0.8644395 0.2544376 0.6848926 0.01950829 0.02874122 0.01384388
## 51 51 0.8646434 0.2541930 0.6850946 0.01986247 0.02912193 0.01419354
## 52 52 0.8647168 0.2540937 0.6851667 0.01988526 0.02897800 0.01426402
## 53 53 0.8650197 0.2536414 0.6853678 0.02002009 0.02965776 0.01437206
## 54 54 0.8651119 0.2534633 0.6852614 0.01970096 0.02947804 0.01412519
## 55 55 0.8652025 0.2533017 0.6852252 0.01977879 0.02915355 0.01420397
## 56 56 0.8652428 0.2532679 0.6852028 0.01970847 0.02938519 0.01419193
## 57 57 0.8653956 0.2530118 0.6853625 0.01959122 0.02897696 0.01416933
## 58 58 0.8655504 0.2528030 0.6853860 0.01973181 0.02955372 0.01437524
## 59 59 0.8657764 0.2524767 0.6854909 0.01969607 0.02973555 0.01394231
## 60 60 0.8655095 0.2529617 0.6854701 0.02007968 0.03058420 0.01397471
## 61 61 0.8654659 0.2530290 0.6854764 0.02000677 0.03069539 0.01392198
## 62 62 0.8653683 0.2531643 0.6852586 0.01998814 0.03042790 0.01365859
## 63 63 0.8652272 0.2533810 0.6850642 0.01992762 0.02996824 0.01357893
## 64 64 0.8653442 0.2532359 0.6852150 0.02005351 0.02996207 0.01391246
## 65 65 0.8656406 0.2527805 0.6852743 0.02010298 0.02977521 0.01392143
## 66 66 0.8658982 0.2523735 0.6853409 0.02012636 0.02959888 0.01381004
## 67 67 0.8664001 0.2516201 0.6853888 0.02027279 0.02956650 0.01407158
## 68 68 0.8667609 0.2510732 0.6859019 0.02002746 0.02927751 0.01396710
## 69 69 0.8667553 0.2511074 0.6861604 0.02032092 0.02962459 0.01386107
## 70 70 0.8666772 0.2512184 0.6862749 0.02037721 0.02924482 0.01393443
## 71 71 0.8669382 0.2508268 0.6863397 0.02005043 0.02859833 0.01375606
## 72 72 0.8669444 0.2508340 0.6862750 0.02019659 0.02905987 0.01378084
## 73 73 0.8668970 0.2509115 0.6861946 0.02048076 0.02927464 0.01370287
## 74 74 0.8671027 0.2506445 0.6865076 0.02053195 0.02958114 0.01376247
## 75 75 0.8671110 0.2506429 0.6865424 0.02035403 0.02863906 0.01364873
## 76 76 0.8672183 0.2505043 0.6867588 0.02052527 0.02857076 0.01392372
## 77 77 0.8667592 0.2512374 0.6862806 0.02076076 0.02876089 0.01416686
## 78 78 0.8671237 0.2506902 0.6866500 0.02076330 0.02897214 0.01402541
## 79 79 0.8672382 0.2505485 0.6867598 0.02068651 0.02870341 0.01401598
## 80 80 0.8672894 0.2504446 0.6868186 0.02055886 0.02854447 0.01394979
## 81 81 0.8675247 0.2501010 0.6869850 0.02042190 0.02874951 0.01401582
## 82 82 0.8677339 0.2497567 0.6872035 0.02048147 0.02878634 0.01401275
## 83 83 0.8679755 0.2494012 0.6874998 0.02047249 0.02892510 0.01387990
## 84 84 0.8682912 0.2488872 0.6878119 0.02050882 0.02868972 0.01390536
## 85 85 0.8684991 0.2485580 0.6879382 0.02050820 0.02873943 0.01383865
## 86 86 0.8685331 0.2485152 0.6880214 0.02053196 0.02851228 0.01411839
## 87 87 0.8686396 0.2483569 0.6879383 0.02045703 0.02845340 0.01409177
## 88 88 0.8687014 0.2483021 0.6880916 0.02069513 0.02894650 0.01416545
## 89 89 0.8687231 0.2482767 0.6881354 0.02091441 0.02914335 0.01425981
## 90 90 0.8686406 0.2484447 0.6879370 0.02099591 0.02929621 0.01427312
## 91 91 0.8687061 0.2483347 0.6880508 0.02109278 0.02916987 0.01446282
## 92 92 0.8689498 0.2479420 0.6882635 0.02125122 0.02925812 0.01466643
## 93 93 0.8689835 0.2478756 0.6882322 0.02097122 0.02912780 0.01449235
## 94 94 0.8691293 0.2476558 0.6884069 0.02096899 0.02932902 0.01459235
## 95 95 0.8690819 0.2477219 0.6883831 0.02107959 0.02965350 0.01452914
## 96 96 0.8692594 0.2474244 0.6883610 0.02099648 0.02950235 0.01457217
## 97 97 0.8691618 0.2475576 0.6883586 0.02083575 0.02932564 0.01441647
## 98 98 0.8692747 0.2473859 0.6883764 0.02062616 0.02904967 0.01426837
## 99 99 0.8692589 0.2474212 0.6883694 0.02054983 0.02905682 0.01427949
## 100 100 0.8694232 0.2471951 0.6886814 0.02076209 0.02922961 0.01458157
## 101 101 0.8693655 0.2472845 0.6886878 0.02065976 0.02926472 0.01461846
## 102 102 0.8694445 0.2471661 0.6888801 0.02090803 0.02956336 0.01468292
## 103 103 0.8692959 0.2473847 0.6887484 0.02086671 0.02942886 0.01466039
## 104 104 0.8696935 0.2467643 0.6891023 0.02082343 0.02924897 0.01480760
## 105 105 0.8697127 0.2467368 0.6891084 0.02080207 0.02929875 0.01476674
## 106 106 0.8698949 0.2464748 0.6892429 0.02099592 0.02939508 0.01483018
## 107 107 0.8700863 0.2461699 0.6893832 0.02111988 0.02965248 0.01472276
## 108 108 0.8703431 0.2457824 0.6895004 0.02118405 0.02975344 0.01468516
## 109 109 0.8705756 0.2454053 0.6896912 0.02119820 0.02952493 0.01477070
## 110 110 0.8706570 0.2452537 0.6897338 0.02117432 0.02950931 0.01458352
## 111 111 0.8706297 0.2452986 0.6896660 0.02112027 0.02959290 0.01456082
## 112 112 0.8705537 0.2454377 0.6896304 0.02107823 0.02957673 0.01462308
## 113 113 0.8705082 0.2455160 0.6896114 0.02097195 0.02944396 0.01453365
## 114 114 0.8705754 0.2454224 0.6897450 0.02090983 0.02920766 0.01461099
## 115 115 0.8705797 0.2454085 0.6897209 0.02083617 0.02914184 0.01456153
## 116 116 0.8705945 0.2453966 0.6897878 0.02082758 0.02909297 0.01461738
## 117 117 0.8706290 0.2453491 0.6898487 0.02079368 0.02916331 0.01450034
## 118 118 0.8706035 0.2454112 0.6897514 0.02068914 0.02909768 0.01438746
## 119 119 0.8706283 0.2454149 0.6898045 0.02080031 0.02921949 0.01436571
## 120 120 0.8707871 0.2451897 0.6898049 0.02084206 0.02942329 0.01433198
## 121 121 0.8709820 0.2448916 0.6898475 0.02080758 0.02961627 0.01431367
## 122 122 0.8709697 0.2449152 0.6898767 0.02081258 0.02949367 0.01433406
## 123 123 0.8710912 0.2447527 0.6899111 0.02082509 0.02945656 0.01436855
## 124 124 0.8711597 0.2446399 0.6900852 0.02071027 0.02925190 0.01435693
## 125 125 0.8712877 0.2444422 0.6900894 0.02083640 0.02923366 0.01441682
## 126 126 0.8713941 0.2442847 0.6901772 0.02081951 0.02926134 0.01431285
## 127 127 0.8712859 0.2444448 0.6901236 0.02071286 0.02914460 0.01424163
## 128 128 0.8712086 0.2445754 0.6900619 0.02078744 0.02919258 0.01423561
## 129 129 0.8710531 0.2448400 0.6899657 0.02086813 0.02941884 0.01412489
## 130 130 0.8711358 0.2447343 0.6900480 0.02093237 0.02945822 0.01410371
## 131 131 0.8712046 0.2446274 0.6900496 0.02089466 0.02926282 0.01423287
## 132 132 0.8711058 0.2447591 0.6900162 0.02084927 0.02903770 0.01424155
## 133 133 0.8710401 0.2448937 0.6899364 0.02091776 0.02902598 0.01427673
## 134 134 0.8710319 0.2449147 0.6899250 0.02094361 0.02910491 0.01426124
## 135 135 0.8711151 0.2447972 0.6899568 0.02064792 0.02861444 0.01406953
## 136 136 0.8712306 0.2446072 0.6901280 0.02052301 0.02832357 0.01405575
## 137 137 0.8712621 0.2445669 0.6901539 0.02058951 0.02851199 0.01410071
## 138 138 0.8710916 0.2448250 0.6899997 0.02065806 0.02836444 0.01408700
## 139 139 0.8712367 0.2446157 0.6901616 0.02055622 0.02811890 0.01397275
## 140 140 0.8711998 0.2446712 0.6901027 0.02060822 0.02799414 0.01406669
## 141 141 0.8713158 0.2444969 0.6901449 0.02071803 0.02823740 0.01396832
## 142 142 0.8713034 0.2445294 0.6901679 0.02069684 0.02819298 0.01408468
## 143 143 0.8713683 0.2444281 0.6901645 0.02060307 0.02802407 0.01401607
## 144 144 0.8713569 0.2444409 0.6902315 0.02056513 0.02818447 0.01396179
## 145 145 0.8713774 0.2444170 0.6902930 0.02054694 0.02828622 0.01386957
## 146 146 0.8712730 0.2445769 0.6902728 0.02044876 0.02807463 0.01375032
## 147 147 0.8712527 0.2446188 0.6901834 0.02050523 0.02801955 0.01384962
## 148 148 0.8710502 0.2449493 0.6900083 0.02047085 0.02795599 0.01381975
## 149 149 0.8711175 0.2448547 0.6901115 0.02055742 0.02817307 0.01379476
## 150 150 0.8711157 0.2448667 0.6901539 0.02067697 0.02827671 0.01387178
## 151 151 0.8710833 0.2449303 0.6901347 0.02072097 0.02829473 0.01391863
## 152 152 0.8711162 0.2448828 0.6901485 0.02075774 0.02835297 0.01392269
## 153 153 0.8711771 0.2447953 0.6902609 0.02086978 0.02843315 0.01397148
## 154 154 0.8712347 0.2447184 0.6902335 0.02091519 0.02848442 0.01412556
## 155 155 0.8711361 0.2448762 0.6901372 0.02093749 0.02842538 0.01415616
## 156 156 0.8712337 0.2447406 0.6901292 0.02108884 0.02847537 0.01421835
## 157 157 0.8711736 0.2448526 0.6900581 0.02105340 0.02850010 0.01409611
## 158 158 0.8711462 0.2448794 0.6901316 0.02100275 0.02838615 0.01421449
## 159 159 0.8712409 0.2447563 0.6901944 0.02112370 0.02860997 0.01424422
## 160 160 0.8712807 0.2446967 0.6901892 0.02120499 0.02877726 0.01425095
## 161 161 0.8711596 0.2448738 0.6900781 0.02119133 0.02875914 0.01428886
## 162 162 0.8712774 0.2446874 0.6901690 0.02116432 0.02872789 0.01428432
## 163 163 0.8711287 0.2449168 0.6900464 0.02123452 0.02876368 0.01437950
## 164 164 0.8710915 0.2449623 0.6900716 0.02118637 0.02877348 0.01436387
## 165 165 0.8711921 0.2448126 0.6902052 0.02125244 0.02886400 0.01437262
## 166 166 0.8711645 0.2448510 0.6902288 0.02124332 0.02881924 0.01430674
## 167 167 0.8712652 0.2447059 0.6903123 0.02132825 0.02892615 0.01434286
## 168 168 0.8712140 0.2447864 0.6902311 0.02127253 0.02885056 0.01423407
## 169 169 0.8711383 0.2449069 0.6901360 0.02130524 0.02909020 0.01423164
## 170 170 0.8711185 0.2449465 0.6900541 0.02135655 0.02917371 0.01428241
## 171 171 0.8711749 0.2448649 0.6901272 0.02133800 0.02929512 0.01418843
## 172 172 0.8711349 0.2449275 0.6900604 0.02132855 0.02948041 0.01408380
## 173 173 0.8711275 0.2449570 0.6900108 0.02133465 0.02960043 0.01408747
## 174 174 0.8711346 0.2449414 0.6900717 0.02124822 0.02958816 0.01399021
## 175 175 0.8711532 0.2449036 0.6901101 0.02119081 0.02942734 0.01395170
## 176 176 0.8711093 0.2449749 0.6900312 0.02112456 0.02933080 0.01392380
## 177 177 0.8710952 0.2449867 0.6900572 0.02106202 0.02927793 0.01389001
## 178 178 0.8710804 0.2450125 0.6900561 0.02113964 0.02942374 0.01395202
## 179 179 0.8710763 0.2450186 0.6900630 0.02119212 0.02947254 0.01396369
## 180 180 0.8710832 0.2450131 0.6900365 0.02123646 0.02942406 0.01398527
## 181 181 0.8710736 0.2450396 0.6900090 0.02113791 0.02930619 0.01390232
## 182 182 0.8710458 0.2450890 0.6899131 0.02109437 0.02929250 0.01385609
## 183 183 0.8710961 0.2450185 0.6899234 0.02112379 0.02929783 0.01387969
## 184 184 0.8710271 0.2451176 0.6898817 0.02103771 0.02919615 0.01384232
## 185 185 0.8710043 0.2451458 0.6898444 0.02103426 0.02911157 0.01377907
## 186 186 0.8710048 0.2451473 0.6898530 0.02103676 0.02912609 0.01376496
## 187 187 0.8709963 0.2451576 0.6898529 0.02108785 0.02924877 0.01377115
## 188 188 0.8709842 0.2451876 0.6899148 0.02111971 0.02927357 0.01389706
## 189 189 0.8709873 0.2451916 0.6899685 0.02109875 0.02922354 0.01395655
## 190 190 0.8710406 0.2451060 0.6899844 0.02109246 0.02924752 0.01401830
## 191 191 0.8709588 0.2452390 0.6899816 0.02109039 0.02916670 0.01400681
## 192 192 0.8709811 0.2451996 0.6899624 0.02107950 0.02912471 0.01398331
## 193 193 0.8709853 0.2451803 0.6899733 0.02103639 0.02904459 0.01395551
## 194 194 0.8709182 0.2452925 0.6898874 0.02110993 0.02912974 0.01402862
## 195 195 0.8709558 0.2452263 0.6899316 0.02113989 0.02907110 0.01404849
## 196 196 0.8709432 0.2452508 0.6898917 0.02111882 0.02903418 0.01403962
## 197 197 0.8709598 0.2452248 0.6898924 0.02111907 0.02904338 0.01403203
## 198 198 0.8709514 0.2452282 0.6899063 0.02117657 0.02909739 0.01408048
## 199 199 0.8709082 0.2452996 0.6898663 0.02114716 0.02903176 0.01406275
## 200 200 0.8709011 0.2453082 0.6898860 0.02110396 0.02898923 0.01401707
## 201 201 0.8709807 0.2451867 0.6899327 0.02110346 0.02900607 0.01405703
## 202 202 0.8708745 0.2453477 0.6898521 0.02107782 0.02899604 0.01402407
## 203 203 0.8708584 0.2453716 0.6898497 0.02105388 0.02894708 0.01398756
## 204 204 0.8708581 0.2453766 0.6898513 0.02105834 0.02890832 0.01399413
## 205 205 0.8708489 0.2454034 0.6898480 0.02109830 0.02899896 0.01403535
## 206 206 0.8708463 0.2454086 0.6898147 0.02109298 0.02900501 0.01405162
## 207 207 0.8708843 0.2453488 0.6898349 0.02108793 0.02894228 0.01407714
## 208 208 0.8708767 0.2453638 0.6898140 0.02106154 0.02888536 0.01407658
## 209 209 0.8708897 0.2453462 0.6898105 0.02106403 0.02890307 0.01404738
## 210 210 0.8708898 0.2453374 0.6898011 0.02101205 0.02885188 0.01403132
## 211 211 0.8709184 0.2452909 0.6898232 0.02100778 0.02883210 0.01400844
## 212 212 0.8709038 0.2453085 0.6897977 0.02097485 0.02878769 0.01396455
## 213 213 0.8709212 0.2452829 0.6898120 0.02099960 0.02874074 0.01397542
## 214 214 0.8709336 0.2452647 0.6898221 0.02095327 0.02868699 0.01393476
## 215 215 0.8709541 0.2452294 0.6898285 0.02093752 0.02866226 0.01393469
## 216 216 0.8709181 0.2452826 0.6898032 0.02091984 0.02862750 0.01391265
## 217 217 0.8709383 0.2452499 0.6898019 0.02090191 0.02857891 0.01389143
## 218 218 0.8709594 0.2452213 0.6898122 0.02092089 0.02859895 0.01389152
## 219 219 0.8709504 0.2452379 0.6898225 0.02095061 0.02860147 0.01392855
## 220 220 0.8709595 0.2452215 0.6898295 0.02091165 0.02857674 0.01389363
## 221 221 0.8709620 0.2452193 0.6898415 0.02091774 0.02861461 0.01390884
## 222 222 0.8709319 0.2452631 0.6898199 0.02092204 0.02862018 0.01392612
## 223 223 0.8709661 0.2452133 0.6898494 0.02091606 0.02863118 0.01390926
## 224 224 0.8709749 0.2451958 0.6898617 0.02087261 0.02860788 0.01387422
## 225 225 0.8709869 0.2451785 0.6898824 0.02088212 0.02864416 0.01388276
## 226 226 0.8709852 0.2451818 0.6898890 0.02087977 0.02866299 0.01387216
## 227 227 0.8709797 0.2451895 0.6898882 0.02086446 0.02866466 0.01385896
## 228 228 0.8709643 0.2452123 0.6898701 0.02087165 0.02869113 0.01385553
## 229 229 0.8709644 0.2452103 0.6898638 0.02085810 0.02867718 0.01384820
## 230 230 0.8709578 0.2452244 0.6898649 0.02087034 0.02869888 0.01386252
## 231 231 0.8709710 0.2452039 0.6898742 0.02086966 0.02867474 0.01386523
## 232 232 0.8709715 0.2452026 0.6898689 0.02086836 0.02866868 0.01385658
## 233 233 0.8709690 0.2452062 0.6898742 0.02086723 0.02865404 0.01385623
## 234 234 0.8709601 0.2452207 0.6898732 0.02087161 0.02865150 0.01385151
## 235 235 0.8709550 0.2452287 0.6898716 0.02086803 0.02864109 0.01384866
## 236 236 0.8709448 0.2452437 0.6898626 0.02086830 0.02862462 0.01385545
## 237 237 0.8709466 0.2452403 0.6898668 0.02087086 0.02862439 0.01386168
## 238 238 0.8709469 0.2452393 0.6898647 0.02086630 0.02861955 0.01386117
## 239 239 0.8709465 0.2452396 0.6898656 0.02086696 0.02862015 0.01386082
## 240 240 0.8709482 0.2452370 0.6898676 0.02086412 0.02861708 0.01385710
## nvmax
## 13 13## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## -3.832583e+00 -1.336998e-03 3.502871e-01 1.316519e-02 1.026206e-01
## x10 x11 x16 x17 x21
## 3.532314e-02 6.547159e+06 2.676086e-02 3.808197e-02 3.363089e-03
## stat14 stat98 stat110 sqrt.x18
## -2.807359e-02 1.008003e-01 -9.721343e-02 7.689960e-01Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.615548 -0.383649 0.009481 -0.010402 0.359098 1.565338
## [1] "leapForward Test MSE: 0.707385129532587"
Forward Selection with CV (w/ filtered train)
Train
if (algo.forward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapForward"
,feature.names = feature.names)
model.forward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 14 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.8409023 0.1577218 0.6879777 0.01268808 0.03072104 0.01129038
## 2 2 0.8033473 0.2319526 0.6608168 0.01175259 0.03593694 0.01051046
## 3 3 0.7837206 0.2693743 0.6423414 0.01806620 0.04565986 0.01381834
## 4 4 0.7609968 0.3105987 0.6196852 0.01993332 0.04660860 0.01470158
## 5 5 0.7484550 0.3326744 0.6098049 0.01972914 0.04767659 0.01512932
## 6 6 0.7445668 0.3396824 0.6073199 0.01970809 0.04865528 0.01514497
## 7 7 0.7443466 0.3399573 0.6072336 0.01890122 0.04736629 0.01403613
## 8 8 0.7424917 0.3430195 0.6063003 0.01804282 0.04515576 0.01382522
## 9 9 0.7401689 0.3468840 0.6048158 0.01731572 0.04337614 0.01408610
## 10 10 0.7365026 0.3532011 0.6026346 0.01616534 0.04154642 0.01295357
## 11 11 0.7376458 0.3512336 0.6032208 0.01601369 0.04073140 0.01253736
## 12 12 0.7380996 0.3503967 0.6034986 0.01642763 0.04172391 0.01255994
## 13 13 0.7375208 0.3514510 0.6030971 0.01701738 0.04144952 0.01306405
## 14 14 0.7363273 0.3535263 0.6023590 0.01659820 0.04066941 0.01275084
## 15 15 0.7363301 0.3534808 0.6018782 0.01553689 0.03820284 0.01210340
## 16 16 0.7366222 0.3530071 0.6022254 0.01480923 0.03657084 0.01189792
## 17 17 0.7368169 0.3526335 0.6025223 0.01472815 0.03541949 0.01199575
## 18 18 0.7363804 0.3533739 0.6021831 0.01378350 0.03436556 0.01122598
## 19 19 0.7373765 0.3516857 0.6027204 0.01397516 0.03415938 0.01164362
## 20 20 0.7373494 0.3517097 0.6024701 0.01386563 0.03336486 0.01206019
## 21 21 0.7372872 0.3518576 0.6025976 0.01408742 0.03349182 0.01227980
## 22 22 0.7369927 0.3524393 0.6019267 0.01364810 0.03301743 0.01192134
## 23 23 0.7367653 0.3529254 0.6016105 0.01457062 0.03384498 0.01290424
## 24 24 0.7373179 0.3520332 0.6020201 0.01431984 0.03381883 0.01270119
## 25 25 0.7372049 0.3523123 0.6019132 0.01440990 0.03406174 0.01270403
## 26 26 0.7373940 0.3519885 0.6019777 0.01413314 0.03327982 0.01275523
## 27 27 0.7375695 0.3517684 0.6020455 0.01428035 0.03361162 0.01273003
## 28 28 0.7377750 0.3514236 0.6020313 0.01457593 0.03423781 0.01271837
## 29 29 0.7378507 0.3513274 0.6022501 0.01512819 0.03441831 0.01320870
## 30 30 0.7376914 0.3516571 0.6023938 0.01488370 0.03420653 0.01322901
## 31 31 0.7378856 0.3513142 0.6024847 0.01433757 0.03334595 0.01300728
## 32 32 0.7379469 0.3511519 0.6025682 0.01442633 0.03329000 0.01335014
## 33 33 0.7378786 0.3513039 0.6023042 0.01450974 0.03341276 0.01341846
## 34 34 0.7376079 0.3517986 0.6022509 0.01501636 0.03388655 0.01359260
## 35 35 0.7378932 0.3513131 0.6025848 0.01498840 0.03467393 0.01371315
## 36 36 0.7380828 0.3510071 0.6025050 0.01514027 0.03474425 0.01366704
## 37 37 0.7381048 0.3510052 0.6023614 0.01483878 0.03383483 0.01331592
## 38 38 0.7378948 0.3513428 0.6023185 0.01460352 0.03346911 0.01328132
## 39 39 0.7374663 0.3520729 0.6020512 0.01430466 0.03349454 0.01263274
## 40 40 0.7372526 0.3524791 0.6018468 0.01458445 0.03361291 0.01254014
## 41 41 0.7377576 0.3516759 0.6021838 0.01488598 0.03346599 0.01302169
## 42 42 0.7377250 0.3517806 0.6019835 0.01530237 0.03346535 0.01323195
## 43 43 0.7382842 0.3508119 0.6024108 0.01519865 0.03247491 0.01346430
## 44 44 0.7378829 0.3514900 0.6022882 0.01531203 0.03279355 0.01333877
## 45 45 0.7374289 0.3522258 0.6020804 0.01507482 0.03263129 0.01296224
## 46 46 0.7376333 0.3519120 0.6023221 0.01489583 0.03187344 0.01300005
## 47 47 0.7378879 0.3515049 0.6024948 0.01540893 0.03201811 0.01329795
## 48 48 0.7376799 0.3518570 0.6026234 0.01489747 0.03167943 0.01287096
## 49 49 0.7378366 0.3516011 0.6025527 0.01527456 0.03153035 0.01305666
## 50 50 0.7377295 0.3518508 0.6026381 0.01560731 0.03181406 0.01342387
## 51 51 0.7378531 0.3517062 0.6028346 0.01604639 0.03294225 0.01371526
## 52 52 0.7374454 0.3523726 0.6024782 0.01582657 0.03276496 0.01305556
## 53 53 0.7376949 0.3519464 0.6028039 0.01605705 0.03284425 0.01346419
## 54 54 0.7374910 0.3523456 0.6025378 0.01626622 0.03295962 0.01401332
## 55 55 0.7376179 0.3521012 0.6026707 0.01631152 0.03299921 0.01431387
## 56 56 0.7377300 0.3519154 0.6028957 0.01644831 0.03348581 0.01444114
## 57 57 0.7378323 0.3517590 0.6030916 0.01667356 0.03360485 0.01452518
## 58 58 0.7381273 0.3512737 0.6031913 0.01685727 0.03365168 0.01456214
## 59 59 0.7384817 0.3506643 0.6035779 0.01666756 0.03356277 0.01442560
## 60 60 0.7386168 0.3504561 0.6035504 0.01668201 0.03364547 0.01449175
## 61 61 0.7385164 0.3507006 0.6033790 0.01666424 0.03359198 0.01436613
## 62 62 0.7384053 0.3509024 0.6031182 0.01683266 0.03364108 0.01444710
## 63 63 0.7386611 0.3504853 0.6031194 0.01694844 0.03391777 0.01457449
## 64 64 0.7388433 0.3501824 0.6033354 0.01678706 0.03399561 0.01414916
## 65 65 0.7394976 0.3491328 0.6037521 0.01659730 0.03379036 0.01415415
## 66 66 0.7396237 0.3489432 0.6039282 0.01672796 0.03360473 0.01426390
## 67 67 0.7393918 0.3493999 0.6038565 0.01698676 0.03380225 0.01458520
## 68 68 0.7391882 0.3497452 0.6035051 0.01678115 0.03335541 0.01423347
## 69 69 0.7393421 0.3494831 0.6035126 0.01670547 0.03366713 0.01412898
## 70 70 0.7390776 0.3499068 0.6032154 0.01660027 0.03379206 0.01431153
## 71 71 0.7390320 0.3500384 0.6032087 0.01658264 0.03406738 0.01427845
## 72 72 0.7390704 0.3499827 0.6033204 0.01647361 0.03390572 0.01406481
## 73 73 0.7393556 0.3495324 0.6037707 0.01637080 0.03420723 0.01410007
## 74 74 0.7393945 0.3494321 0.6038196 0.01623161 0.03364671 0.01385869
## 75 75 0.7394061 0.3494062 0.6037594 0.01588029 0.03246240 0.01371690
## 76 76 0.7394101 0.3494216 0.6036882 0.01602458 0.03315104 0.01377992
## 77 77 0.7392948 0.3495968 0.6037023 0.01595436 0.03318927 0.01391237
## 78 78 0.7391376 0.3498437 0.6035905 0.01551796 0.03250127 0.01342401
## 79 79 0.7391141 0.3499324 0.6035568 0.01546985 0.03223502 0.01335025
## 80 80 0.7391983 0.3498254 0.6035858 0.01557878 0.03251788 0.01342815
## 81 81 0.7394632 0.3493566 0.6038344 0.01562631 0.03244089 0.01335892
## 82 82 0.7396552 0.3490592 0.6040567 0.01551413 0.03236259 0.01361742
## 83 83 0.7397361 0.3489413 0.6040484 0.01538773 0.03227724 0.01387407
## 84 84 0.7400190 0.3484906 0.6041007 0.01534481 0.03244851 0.01377688
## 85 85 0.7399730 0.3485427 0.6040688 0.01537103 0.03253281 0.01370340
## 86 86 0.7402330 0.3481140 0.6043211 0.01509720 0.03194662 0.01347058
## 87 87 0.7399468 0.3485819 0.6041069 0.01513580 0.03204793 0.01359579
## 88 88 0.7400412 0.3484259 0.6042029 0.01531371 0.03242480 0.01370866
## 89 89 0.7400726 0.3483668 0.6042542 0.01501019 0.03213114 0.01345348
## 90 90 0.7399350 0.3485624 0.6042765 0.01495223 0.03191414 0.01328022
## 91 91 0.7400486 0.3483833 0.6042815 0.01487719 0.03182798 0.01320196
## 92 92 0.7398855 0.3487168 0.6040968 0.01482213 0.03164555 0.01318859
## 93 93 0.7402665 0.3481175 0.6045246 0.01479356 0.03121699 0.01314971
## 94 94 0.7403956 0.3479138 0.6047259 0.01480305 0.03151144 0.01323469
## 95 95 0.7404614 0.3478059 0.6048156 0.01488469 0.03182473 0.01317060
## 96 96 0.7405442 0.3476607 0.6049880 0.01509268 0.03189307 0.01317486
## 97 97 0.7404041 0.3478886 0.6048793 0.01518425 0.03227635 0.01322625
## 98 98 0.7403376 0.3480042 0.6048031 0.01532138 0.03230502 0.01328243
## 99 99 0.7401827 0.3482994 0.6046496 0.01486137 0.03204957 0.01289540
## 100 100 0.7400857 0.3484525 0.6045616 0.01481207 0.03191564 0.01295719
## 101 101 0.7401926 0.3482645 0.6047691 0.01495039 0.03212990 0.01296308
## 102 102 0.7399680 0.3486326 0.6046513 0.01473494 0.03167372 0.01270246
## 103 103 0.7401335 0.3483619 0.6047827 0.01424690 0.03120442 0.01232457
## 104 104 0.7400752 0.3484849 0.6046366 0.01428013 0.03120338 0.01232267
## 105 105 0.7401760 0.3483049 0.6046925 0.01426414 0.03096810 0.01230321
## 106 106 0.7403650 0.3479656 0.6049233 0.01418876 0.03112409 0.01216154
## 107 107 0.7406721 0.3474872 0.6052454 0.01427115 0.03123553 0.01229226
## 108 108 0.7404726 0.3478144 0.6051806 0.01449343 0.03136863 0.01240524
## 109 109 0.7404297 0.3478805 0.6050557 0.01426130 0.03129106 0.01212624
## 110 110 0.7404744 0.3478154 0.6051349 0.01415329 0.03126182 0.01199968
## 111 111 0.7405905 0.3476459 0.6052344 0.01447955 0.03173280 0.01224684
## 112 112 0.7406641 0.3475541 0.6051837 0.01438562 0.03164378 0.01217678
## 113 113 0.7407282 0.3474577 0.6052902 0.01434063 0.03124113 0.01230970
## 114 114 0.7406321 0.3476200 0.6052920 0.01426848 0.03152397 0.01238254
## 115 115 0.7403797 0.3480315 0.6050552 0.01412593 0.03142046 0.01223857
## 116 116 0.7404286 0.3479250 0.6049863 0.01390161 0.03132668 0.01215123
## 117 117 0.7402902 0.3481511 0.6047545 0.01378987 0.03102504 0.01211245
## 118 118 0.7404220 0.3479552 0.6049435 0.01388729 0.03123744 0.01226338
## 119 119 0.7404244 0.3479436 0.6049393 0.01406794 0.03119171 0.01244603
## 120 120 0.7403340 0.3480645 0.6048932 0.01399137 0.03113171 0.01248198
## 121 121 0.7403054 0.3481067 0.6048640 0.01420235 0.03165626 0.01278894
## 122 122 0.7402291 0.3482787 0.6048668 0.01433633 0.03176540 0.01278824
## 123 123 0.7401002 0.3484981 0.6047184 0.01464058 0.03202343 0.01314665
## 124 124 0.7400681 0.3485446 0.6047511 0.01452832 0.03174295 0.01318153
## 125 125 0.7403252 0.3481154 0.6048576 0.01456212 0.03172349 0.01310864
## 126 126 0.7404818 0.3478893 0.6049314 0.01449742 0.03171828 0.01309146
## 127 127 0.7406465 0.3476243 0.6049670 0.01434997 0.03152348 0.01293378
## 128 128 0.7406775 0.3476046 0.6050013 0.01435298 0.03153023 0.01295351
## 129 129 0.7407795 0.3474505 0.6050926 0.01455418 0.03131525 0.01311916
## 130 130 0.7407557 0.3474788 0.6051056 0.01461832 0.03141698 0.01317153
## 131 131 0.7408605 0.3473356 0.6052207 0.01478594 0.03191888 0.01327466
## 132 132 0.7411277 0.3468813 0.6054199 0.01481102 0.03183686 0.01331079
## 133 133 0.7413932 0.3464442 0.6057261 0.01463699 0.03154826 0.01318306
## 134 134 0.7416185 0.3460759 0.6058591 0.01456647 0.03158432 0.01307182
## 135 135 0.7415010 0.3462904 0.6056988 0.01453559 0.03144616 0.01298026
## 136 136 0.7415211 0.3462449 0.6056764 0.01446965 0.03140386 0.01279437
## 137 137 0.7414934 0.3463065 0.6057059 0.01455253 0.03138836 0.01293713
## 138 138 0.7415610 0.3462287 0.6056469 0.01473020 0.03165008 0.01310739
## 139 139 0.7415924 0.3461772 0.6056406 0.01480646 0.03143874 0.01309670
## 140 140 0.7416516 0.3460831 0.6056587 0.01493133 0.03149358 0.01322962
## 141 141 0.7417051 0.3460161 0.6056058 0.01508813 0.03147926 0.01335005
## 142 142 0.7416298 0.3461343 0.6054965 0.01514457 0.03141511 0.01337996
## 143 143 0.7418121 0.3458547 0.6056648 0.01523791 0.03132653 0.01349497
## 144 144 0.7420042 0.3455411 0.6058228 0.01522371 0.03129664 0.01339135
## 145 145 0.7420012 0.3455299 0.6057338 0.01525492 0.03149603 0.01341767
## 146 146 0.7421049 0.3453567 0.6058472 0.01533352 0.03167308 0.01353283
## 147 147 0.7421999 0.3451971 0.6059699 0.01559763 0.03175736 0.01375951
## 148 148 0.7422219 0.3451547 0.6059013 0.01559600 0.03160652 0.01373256
## 149 149 0.7421719 0.3452392 0.6058335 0.01556997 0.03170939 0.01371660
## 150 150 0.7423998 0.3448690 0.6060640 0.01548940 0.03149813 0.01367436
## 151 151 0.7425577 0.3446069 0.6061831 0.01526399 0.03134183 0.01347205
## 152 152 0.7426242 0.3444803 0.6062175 0.01519254 0.03130991 0.01352158
## 153 153 0.7426153 0.3444849 0.6061703 0.01505661 0.03116448 0.01341032
## 154 154 0.7425889 0.3445279 0.6060987 0.01507235 0.03119754 0.01340194
## 155 155 0.7426537 0.3444161 0.6060577 0.01503211 0.03113764 0.01336246
## 156 156 0.7428219 0.3441394 0.6061686 0.01502309 0.03113957 0.01331233
## 157 157 0.7428600 0.3440786 0.6061170 0.01506897 0.03120300 0.01334845
## 158 158 0.7428506 0.3441142 0.6060850 0.01489865 0.03106978 0.01322327
## 159 159 0.7428709 0.3440968 0.6060464 0.01467978 0.03084769 0.01307693
## 160 160 0.7429358 0.3439842 0.6060601 0.01474522 0.03088552 0.01317890
## 161 161 0.7430546 0.3437944 0.6062223 0.01477976 0.03107018 0.01322134
## 162 162 0.7430585 0.3438026 0.6062239 0.01491060 0.03126730 0.01341688
## 163 163 0.7429418 0.3440144 0.6060910 0.01489539 0.03121015 0.01336188
## 164 164 0.7429847 0.3439597 0.6060606 0.01498762 0.03129350 0.01341550
## 165 165 0.7429476 0.3440354 0.6059807 0.01499182 0.03138948 0.01341150
## 166 166 0.7429303 0.3440589 0.6059932 0.01499408 0.03129088 0.01338146
## 167 167 0.7429537 0.3440297 0.6060139 0.01511188 0.03154950 0.01349461
## 168 168 0.7430168 0.3439383 0.6060148 0.01516052 0.03163326 0.01346847
## 169 169 0.7431068 0.3438150 0.6060803 0.01527207 0.03168420 0.01356663
## 170 170 0.7431074 0.3438231 0.6060972 0.01520732 0.03161898 0.01354056
## 171 171 0.7431283 0.3437906 0.6061215 0.01529601 0.03182491 0.01357445
## 172 172 0.7431369 0.3437854 0.6060850 0.01522843 0.03162933 0.01353545
## 173 173 0.7431679 0.3437390 0.6061060 0.01522405 0.03161282 0.01359088
## 174 174 0.7431786 0.3437256 0.6060851 0.01524417 0.03167552 0.01361470
## 175 175 0.7432332 0.3436323 0.6060802 0.01514649 0.03161303 0.01348709
## 176 176 0.7431701 0.3437210 0.6060365 0.01509607 0.03152481 0.01338757
## 177 177 0.7432192 0.3436462 0.6060632 0.01510626 0.03147450 0.01342325
## 178 178 0.7432090 0.3436646 0.6060605 0.01507076 0.03142510 0.01333675
## 179 179 0.7433050 0.3435124 0.6061500 0.01507685 0.03137569 0.01333094
## 180 180 0.7433533 0.3434386 0.6062052 0.01509958 0.03142680 0.01336219
## 181 181 0.7433346 0.3434778 0.6061942 0.01526759 0.03162916 0.01349042
## 182 182 0.7434409 0.3433029 0.6062563 0.01528984 0.03159930 0.01349206
## 183 183 0.7434708 0.3432642 0.6063523 0.01539224 0.03166880 0.01356686
## 184 184 0.7435729 0.3430991 0.6064001 0.01546068 0.03177531 0.01361543
## 185 185 0.7436355 0.3430069 0.6064958 0.01550938 0.03179894 0.01362611
## 186 186 0.7436350 0.3430028 0.6064829 0.01553884 0.03179766 0.01361664
## 187 187 0.7435937 0.3430723 0.6064326 0.01560876 0.03187406 0.01366571
## 188 188 0.7436434 0.3429871 0.6064586 0.01556121 0.03193682 0.01358963
## 189 189 0.7436914 0.3429218 0.6065077 0.01553542 0.03197761 0.01355191
## 190 190 0.7436667 0.3429584 0.6065156 0.01555851 0.03199734 0.01352159
## 191 191 0.7436313 0.3429972 0.6064712 0.01538935 0.03188922 0.01337729
## 192 192 0.7436527 0.3429690 0.6064792 0.01537903 0.03185653 0.01337860
## 193 193 0.7436626 0.3429559 0.6064995 0.01534510 0.03167674 0.01331683
## 194 194 0.7436417 0.3429944 0.6064703 0.01532142 0.03169376 0.01329805
## 195 195 0.7435992 0.3430573 0.6064121 0.01528537 0.03158903 0.01330234
## 196 196 0.7435270 0.3431734 0.6063406 0.01521672 0.03152423 0.01329800
## 197 197 0.7435512 0.3431413 0.6063894 0.01519308 0.03149038 0.01325151
## 198 198 0.7435452 0.3431553 0.6063952 0.01524788 0.03164432 0.01330733
## 199 199 0.7435473 0.3431585 0.6063937 0.01525021 0.03157697 0.01329670
## 200 200 0.7435602 0.3431441 0.6063728 0.01528041 0.03165486 0.01332133
## 201 201 0.7436069 0.3430679 0.6063970 0.01528882 0.03168416 0.01331720
## 202 202 0.7436039 0.3430762 0.6063747 0.01537728 0.03181580 0.01341025
## 203 203 0.7436129 0.3430644 0.6063610 0.01532804 0.03179156 0.01336434
## 204 204 0.7436245 0.3430470 0.6064024 0.01538031 0.03181569 0.01343854
## 205 205 0.7436023 0.3430820 0.6063737 0.01535258 0.03186156 0.01338926
## 206 206 0.7436521 0.3430004 0.6064292 0.01535949 0.03193848 0.01339256
## 207 207 0.7436801 0.3429593 0.6064461 0.01537046 0.03196902 0.01340616
## 208 208 0.7436879 0.3429550 0.6064336 0.01540096 0.03200618 0.01341149
## 209 209 0.7437029 0.3429358 0.6064374 0.01540072 0.03193734 0.01341080
## 210 210 0.7437139 0.3429168 0.6064476 0.01535214 0.03190807 0.01339077
## 211 211 0.7437118 0.3429198 0.6064465 0.01538974 0.03192214 0.01340304
## 212 212 0.7437175 0.3429070 0.6064385 0.01537155 0.03190504 0.01337462
## 213 213 0.7437465 0.3428555 0.6064520 0.01537442 0.03189626 0.01338156
## 214 214 0.7437286 0.3428859 0.6064303 0.01537480 0.03188941 0.01335691
## 215 215 0.7436922 0.3429395 0.6063816 0.01534676 0.03180363 0.01332834
## 216 216 0.7436732 0.3429675 0.6063696 0.01530897 0.03176279 0.01330174
## 217 217 0.7436631 0.3429866 0.6063876 0.01530102 0.03177383 0.01329785
## 218 218 0.7436976 0.3429273 0.6064206 0.01531310 0.03182881 0.01331794
## 219 219 0.7437012 0.3429249 0.6064269 0.01531953 0.03183132 0.01332384
## 220 220 0.7436988 0.3429267 0.6064309 0.01531341 0.03185490 0.01330329
## 221 221 0.7437093 0.3429110 0.6064433 0.01535698 0.03193747 0.01335169
## 222 222 0.7436958 0.3429315 0.6064429 0.01536589 0.03196301 0.01337918
## 223 223 0.7436896 0.3429430 0.6064373 0.01537708 0.03199155 0.01339421
## 224 224 0.7436885 0.3429466 0.6064327 0.01536950 0.03198249 0.01338616
## 225 225 0.7436762 0.3429667 0.6064264 0.01534809 0.03197904 0.01335714
## 226 226 0.7436865 0.3429481 0.6064388 0.01536621 0.03202983 0.01335675
## 227 227 0.7437125 0.3429066 0.6064591 0.01536721 0.03201308 0.01336770
## 228 228 0.7437030 0.3429207 0.6064592 0.01535874 0.03200725 0.01336021
## 229 229 0.7437160 0.3428982 0.6064722 0.01536325 0.03199914 0.01336798
## 230 230 0.7437152 0.3428996 0.6064765 0.01536388 0.03201147 0.01336086
## 231 231 0.7437020 0.3429207 0.6064602 0.01536391 0.03201815 0.01335912
## 232 232 0.7437026 0.3429211 0.6064543 0.01537004 0.03202617 0.01334967
## 233 233 0.7437095 0.3429094 0.6064650 0.01538046 0.03202637 0.01336097
## 234 234 0.7437014 0.3429225 0.6064605 0.01537147 0.03201019 0.01335536
## 235 235 0.7437075 0.3429118 0.6064675 0.01537769 0.03201645 0.01336572
## 236 236 0.7437075 0.3429119 0.6064647 0.01537818 0.03201369 0.01336517
## 237 237 0.7437101 0.3429086 0.6064644 0.01538230 0.03202068 0.01336713
## 238 238 0.7437119 0.3429055 0.6064625 0.01538549 0.03202128 0.01336905
## 239 239 0.7437121 0.3429051 0.6064628 0.01538583 0.03202209 0.01337002
## 240 240 0.7437119 0.3429053 0.6064635 0.01538568 0.03202256 0.01336963
## nvmax
## 14 14## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## -4.004464e+00 -1.635465e-03 3.772266e-01 1.303228e-02 1.049647e-01
## x10 x11 x16 x17 x21
## 4.298559e-02 6.272044e+06 2.657518e-02 4.181081e-02 3.752106e-03
## stat14 stat41 stat98 stat110 sqrt.x18
## -3.246523e-02 -1.862469e-02 1.066125e-01 -1.001554e-01 7.964291e-01Test
if (algo.forward.caret == TRUE){
test.model(model.forward, data.test
,method = 'leapForward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.75656 -0.45584 -0.04926 -0.06832 0.31879 1.61618
## [1] "leapForward Test MSE: 0.71219103708358"
Backward Elimination
Train
if (algo.backward == TRUE){
# Takes too much time
t1 = Sys.time()
model.backward = step(model.full, data = data.train, direction="backward", trace = 0)
print(summary(model.backward))
#saveRDS(model.forward,file = "model_backward.rds")
t2 = Sys.time()
print (paste("Time taken for Backward Elimination: ",t2-t1, sep = ""))
plot.diagnostics(model.backward, data.train)
}Test
if (algo.backward == TRUE){
test.model(model.backard, data.test, "Backward Elimination")
}Backward Elimination with CV (w/ full train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 13 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.9365643 0.1236737 0.7536160 0.01668298 0.01993896 0.01279423
## 2 2 0.9079494 0.1770387 0.7298664 0.01750665 0.02892992 0.01357292
## 3 3 0.8944193 0.2008249 0.7155952 0.01901631 0.02919065 0.01320105
## 4 4 0.8742691 0.2363913 0.6937420 0.02185141 0.03097169 0.01484363
## 5 5 0.8638186 0.2544505 0.6856338 0.02338283 0.03597771 0.01555964
## 6 6 0.8614669 0.2584423 0.6838031 0.02330853 0.03655409 0.01630994
## 7 7 0.8616264 0.2581201 0.6838827 0.02252992 0.03576400 0.01543925
## 8 8 0.8606372 0.2598909 0.6832436 0.02346111 0.03617819 0.01574605
## 9 9 0.8589201 0.2629713 0.6817973 0.02303138 0.03595457 0.01457108
## 10 10 0.8560569 0.2677599 0.6802123 0.02220196 0.03458445 0.01366388
## 11 11 0.8566254 0.2668271 0.6804815 0.02260781 0.03462453 0.01470736
## 12 12 0.8560859 0.2677589 0.6800820 0.02254281 0.03351252 0.01463182
## 13 13 0.8555747 0.2686228 0.6800973 0.02206305 0.03335563 0.01510989
## 14 14 0.8560136 0.2678391 0.6807736 0.02126001 0.03146312 0.01439859
## 15 15 0.8563294 0.2673560 0.6807459 0.02133984 0.03211955 0.01446735
## 16 16 0.8570050 0.2661981 0.6809689 0.02128136 0.03145458 0.01464079
## 17 17 0.8578058 0.2648974 0.6805910 0.02202643 0.03156827 0.01527678
## 18 18 0.8587239 0.2633832 0.6814539 0.02185671 0.03192988 0.01480811
## 19 19 0.8589868 0.2629528 0.6816334 0.02177558 0.03061139 0.01507559
## 20 20 0.8594587 0.2621429 0.6820007 0.02169319 0.03064491 0.01488072
## 21 21 0.8592165 0.2625859 0.6817450 0.02122121 0.03049068 0.01430862
## 22 22 0.8596630 0.2618405 0.6820149 0.02058561 0.02990860 0.01377926
## 23 23 0.8599657 0.2613521 0.6822373 0.01991861 0.02926440 0.01327024
## 24 24 0.8600141 0.2613129 0.6820905 0.02005046 0.02918015 0.01359636
## 25 25 0.8596311 0.2619713 0.6818048 0.01974444 0.02921075 0.01345087
## 26 26 0.8596415 0.2619258 0.6820984 0.01988885 0.02932988 0.01349023
## 27 27 0.8601784 0.2610319 0.6828302 0.01960764 0.02864057 0.01344128
## 28 28 0.8604082 0.2606614 0.6831892 0.01936522 0.02824562 0.01328344
## 29 29 0.8605567 0.2604734 0.6834480 0.01942296 0.02899682 0.01300198
## 30 30 0.8606018 0.2604001 0.6832609 0.01898791 0.02857932 0.01277832
## 31 31 0.8612467 0.2593023 0.6839339 0.01930531 0.02858065 0.01320908
## 32 32 0.8614199 0.2590512 0.6837813 0.01910704 0.02868758 0.01322392
## 33 33 0.8616403 0.2586923 0.6839795 0.01931191 0.02926995 0.01371658
## 34 34 0.8618072 0.2584278 0.6840556 0.01911459 0.02897868 0.01353975
## 35 35 0.8620435 0.2581013 0.6843567 0.01928940 0.02864242 0.01352513
## 36 36 0.8622495 0.2577702 0.6843889 0.01940811 0.02880614 0.01344987
## 37 37 0.8624435 0.2574835 0.6844280 0.01968755 0.02931613 0.01353697
## 38 38 0.8623582 0.2576082 0.6843283 0.01965300 0.02936515 0.01368578
## 39 39 0.8624024 0.2575331 0.6843141 0.01959292 0.02953076 0.01387019
## 40 40 0.8625883 0.2572104 0.6845694 0.01946659 0.02916949 0.01391406
## 41 41 0.8631788 0.2562752 0.6850405 0.01917295 0.02919017 0.01366859
## 42 42 0.8634754 0.2558213 0.6852612 0.01910378 0.02959159 0.01363498
## 43 43 0.8636613 0.2555304 0.6852846 0.01920377 0.02970908 0.01368077
## 44 44 0.8637282 0.2555082 0.6852873 0.01952407 0.02981430 0.01390162
## 45 45 0.8636906 0.2555941 0.6848678 0.01986005 0.02986920 0.01432617
## 46 46 0.8639723 0.2551614 0.6850879 0.01958558 0.03023105 0.01435965
## 47 47 0.8641834 0.2548092 0.6851931 0.01959379 0.02954692 0.01427494
## 48 48 0.8644121 0.2544614 0.6853580 0.01965992 0.02947512 0.01397826
## 49 49 0.8643245 0.2546313 0.6851708 0.01983891 0.02931367 0.01409606
## 50 50 0.8643579 0.2546082 0.6849973 0.01991314 0.02917845 0.01413896
## 51 51 0.8646171 0.2542466 0.6850583 0.01987016 0.02925354 0.01413039
## 52 52 0.8649418 0.2537512 0.6852799 0.01990932 0.02914966 0.01418377
## 53 53 0.8651187 0.2534767 0.6854653 0.01999842 0.02974757 0.01424932
## 54 54 0.8653182 0.2531340 0.6853590 0.01970101 0.02933692 0.01413195
## 55 55 0.8651745 0.2533593 0.6853175 0.01976652 0.02917789 0.01427693
## 56 56 0.8651066 0.2535013 0.6850555 0.01956409 0.02945799 0.01415917
## 57 57 0.8650266 0.2536217 0.6849551 0.01943408 0.02922926 0.01426336
## 58 58 0.8652509 0.2533027 0.6851302 0.01964316 0.02977978 0.01434250
## 59 59 0.8655877 0.2528090 0.6852290 0.01971564 0.02995004 0.01443446
## 60 60 0.8651411 0.2535738 0.6850008 0.02007599 0.03061746 0.01474648
## 61 61 0.8651878 0.2535046 0.6847487 0.02009185 0.03088458 0.01480147
## 62 62 0.8651009 0.2536122 0.6845954 0.01985088 0.03037240 0.01447021
## 63 63 0.8652732 0.2533212 0.6846163 0.01985302 0.02975371 0.01418246
## 64 64 0.8655743 0.2528848 0.6850243 0.02009465 0.03003344 0.01404808
## 65 65 0.8657058 0.2527027 0.6851630 0.02009702 0.02984747 0.01414860
## 66 66 0.8660804 0.2521008 0.6854923 0.01984088 0.02935869 0.01387186
## 67 67 0.8663039 0.2517924 0.6855992 0.01997432 0.02954874 0.01395594
## 68 68 0.8664655 0.2515667 0.6857538 0.01993123 0.02956265 0.01394807
## 69 69 0.8667758 0.2510711 0.6860430 0.01977697 0.02898650 0.01371124
## 70 70 0.8669990 0.2507069 0.6862663 0.01960530 0.02887835 0.01360332
## 71 71 0.8669769 0.2507896 0.6862602 0.02021346 0.02927246 0.01395888
## 72 72 0.8669798 0.2507606 0.6863925 0.02016520 0.02864704 0.01398512
## 73 73 0.8671082 0.2505710 0.6864267 0.01990594 0.02859050 0.01384779
## 74 74 0.8670545 0.2506987 0.6862886 0.01982708 0.02809689 0.01392881
## 75 75 0.8671834 0.2505173 0.6863800 0.01994594 0.02787082 0.01411895
## 76 76 0.8673139 0.2503441 0.6865654 0.02032950 0.02841831 0.01433087
## 77 77 0.8671478 0.2505929 0.6865048 0.02031057 0.02821585 0.01415583
## 78 78 0.8672619 0.2504626 0.6866454 0.02057055 0.02836735 0.01425723
## 79 79 0.8672697 0.2504675 0.6866593 0.02048630 0.02831568 0.01412148
## 80 80 0.8673461 0.2503354 0.6866620 0.02033219 0.02814677 0.01415362
## 81 81 0.8676538 0.2498809 0.6869297 0.02054122 0.02816786 0.01429340
## 82 82 0.8677684 0.2496864 0.6872315 0.02050042 0.02833130 0.01412270
## 83 83 0.8680234 0.2493009 0.6875237 0.02048851 0.02847641 0.01402754
## 84 84 0.8681408 0.2491012 0.6877016 0.02045192 0.02870687 0.01401450
## 85 85 0.8684249 0.2486827 0.6878645 0.02036558 0.02858416 0.01385894
## 86 86 0.8684295 0.2486964 0.6878914 0.02031005 0.02831252 0.01403434
## 87 87 0.8684231 0.2487130 0.6877863 0.02037673 0.02843159 0.01392244
## 88 88 0.8687012 0.2483006 0.6879341 0.02067387 0.02892182 0.01403264
## 89 89 0.8687366 0.2482522 0.6880747 0.02090900 0.02892512 0.01424256
## 90 90 0.8686152 0.2484404 0.6878444 0.02092654 0.02893318 0.01419869
## 91 91 0.8687082 0.2482928 0.6879710 0.02090623 0.02885509 0.01416358
## 92 92 0.8688310 0.2480838 0.6880724 0.02097849 0.02930971 0.01412548
## 93 93 0.8687434 0.2482475 0.6881554 0.02088629 0.02940647 0.01413087
## 94 94 0.8689641 0.2479182 0.6883991 0.02084887 0.02952348 0.01406476
## 95 95 0.8691168 0.2476637 0.6884143 0.02084641 0.02946343 0.01410450
## 96 96 0.8691215 0.2476573 0.6883300 0.02090486 0.02987236 0.01412492
## 97 97 0.8692286 0.2474543 0.6884753 0.02080377 0.02935685 0.01433756
## 98 98 0.8692918 0.2473472 0.6884991 0.02065578 0.02913221 0.01441377
## 99 99 0.8693945 0.2471866 0.6885239 0.02057941 0.02871775 0.01447196
## 100 100 0.8694070 0.2471820 0.6886260 0.02060200 0.02875679 0.01455577
## 101 101 0.8695626 0.2469518 0.6887808 0.02071882 0.02901436 0.01470188
## 102 102 0.8694424 0.2471672 0.6887476 0.02089387 0.02938671 0.01484390
## 103 103 0.8694245 0.2471735 0.6887179 0.02074125 0.02901172 0.01468136
## 104 104 0.8697781 0.2466338 0.6890031 0.02083421 0.02923737 0.01456960
## 105 105 0.8697800 0.2466239 0.6889986 0.02083091 0.02932740 0.01454599
## 106 106 0.8700353 0.2462445 0.6891863 0.02104588 0.02952956 0.01462267
## 107 107 0.8701177 0.2460971 0.6891338 0.02111483 0.02951981 0.01459164
## 108 108 0.8700693 0.2462098 0.6891640 0.02115808 0.02973450 0.01458971
## 109 109 0.8702427 0.2459299 0.6893752 0.02111730 0.02964118 0.01458730
## 110 110 0.8702009 0.2459816 0.6894865 0.02104191 0.02930547 0.01452487
## 111 111 0.8702717 0.2458818 0.6895196 0.02099416 0.02921412 0.01452344
## 112 112 0.8702878 0.2458833 0.6895516 0.02100260 0.02931748 0.01462306
## 113 113 0.8704051 0.2456967 0.6895528 0.02089086 0.02883122 0.01466771
## 114 114 0.8704660 0.2456157 0.6896848 0.02085868 0.02873692 0.01476646
## 115 115 0.8705691 0.2454582 0.6897086 0.02080466 0.02898123 0.01469216
## 116 116 0.8704245 0.2456811 0.6897001 0.02090107 0.02898495 0.01477699
## 117 117 0.8705337 0.2455288 0.6896877 0.02090064 0.02914250 0.01467981
## 118 118 0.8705972 0.2454513 0.6897489 0.02084018 0.02906281 0.01461465
## 119 119 0.8706015 0.2454685 0.6897421 0.02093643 0.02948109 0.01450446
## 120 120 0.8709149 0.2449906 0.6898817 0.02091743 0.02955269 0.01443893
## 121 121 0.8709436 0.2449621 0.6899169 0.02079003 0.02946770 0.01437891
## 122 122 0.8708749 0.2450743 0.6899414 0.02080614 0.02947978 0.01438911
## 123 123 0.8710680 0.2447978 0.6900101 0.02089320 0.02952087 0.01445114
## 124 124 0.8712810 0.2444629 0.6902011 0.02080993 0.02941678 0.01436672
## 125 125 0.8714417 0.2442130 0.6903130 0.02098838 0.02966022 0.01445358
## 126 126 0.8714014 0.2442779 0.6902024 0.02081466 0.02928450 0.01427327
## 127 127 0.8712901 0.2444463 0.6900936 0.02069467 0.02909246 0.01416436
## 128 128 0.8711833 0.2446206 0.6899250 0.02071078 0.02898921 0.01409420
## 129 129 0.8711714 0.2446637 0.6900166 0.02081552 0.02916502 0.01406494
## 130 130 0.8711050 0.2447754 0.6899504 0.02081981 0.02910637 0.01409353
## 131 131 0.8712194 0.2446082 0.6900343 0.02090774 0.02931705 0.01422291
## 132 132 0.8711530 0.2447067 0.6900396 0.02085214 0.02918593 0.01430432
## 133 133 0.8711106 0.2447817 0.6899654 0.02099022 0.02932377 0.01426931
## 134 134 0.8711977 0.2446529 0.6899804 0.02083924 0.02899918 0.01423720
## 135 135 0.8713043 0.2445005 0.6900692 0.02057948 0.02855585 0.01402916
## 136 136 0.8712761 0.2445444 0.6900407 0.02054929 0.02833556 0.01408714
## 137 137 0.8712474 0.2445959 0.6900202 0.02067464 0.02858343 0.01403636
## 138 138 0.8711912 0.2446927 0.6898919 0.02075832 0.02843381 0.01418100
## 139 139 0.8713295 0.2444960 0.6900917 0.02064746 0.02816696 0.01407152
## 140 140 0.8712383 0.2446463 0.6900928 0.02074840 0.02833721 0.01403621
## 141 141 0.8713070 0.2445447 0.6900811 0.02069215 0.02809597 0.01395169
## 142 142 0.8712645 0.2446192 0.6899888 0.02065677 0.02802275 0.01398336
## 143 143 0.8713356 0.2444983 0.6899969 0.02059458 0.02810271 0.01393127
## 144 144 0.8713169 0.2445082 0.6901234 0.02055057 0.02821877 0.01390427
## 145 145 0.8713925 0.2444003 0.6902265 0.02055085 0.02829057 0.01382527
## 146 146 0.8713433 0.2444627 0.6903059 0.02046646 0.02802768 0.01376804
## 147 147 0.8712520 0.2446196 0.6902213 0.02051285 0.02803622 0.01380768
## 148 148 0.8711324 0.2448208 0.6901144 0.02039050 0.02791873 0.01364614
## 149 149 0.8711975 0.2447368 0.6902198 0.02052032 0.02816039 0.01379495
## 150 150 0.8711879 0.2447435 0.6902846 0.02055890 0.02814802 0.01381923
## 151 151 0.8710743 0.2449411 0.6901716 0.02054134 0.02820099 0.01382262
## 152 152 0.8711917 0.2447529 0.6901754 0.02056990 0.02821888 0.01395043
## 153 153 0.8712597 0.2446552 0.6902587 0.02073971 0.02838072 0.01408055
## 154 154 0.8712807 0.2446489 0.6902880 0.02089951 0.02845465 0.01416981
## 155 155 0.8711507 0.2448433 0.6901109 0.02096027 0.02846472 0.01415984
## 156 156 0.8711705 0.2448291 0.6900400 0.02100605 0.02843497 0.01419098
## 157 157 0.8711036 0.2449448 0.6899937 0.02100978 0.02846470 0.01413015
## 158 158 0.8711764 0.2448273 0.6901460 0.02100624 0.02840651 0.01420823
## 159 159 0.8712169 0.2447865 0.6901696 0.02112536 0.02858491 0.01424374
## 160 160 0.8712945 0.2446766 0.6902077 0.02123372 0.02882578 0.01424542
## 161 161 0.8712094 0.2447998 0.6900654 0.02125411 0.02877984 0.01434739
## 162 162 0.8712629 0.2447297 0.6901386 0.02122538 0.02882386 0.01434178
## 163 163 0.8711825 0.2448439 0.6901538 0.02130267 0.02885490 0.01447740
## 164 164 0.8711829 0.2448342 0.6901762 0.02127197 0.02881170 0.01444986
## 165 165 0.8712576 0.2447245 0.6902907 0.02130658 0.02887700 0.01441796
## 166 166 0.8713113 0.2446355 0.6903306 0.02136225 0.02894989 0.01437565
## 167 167 0.8712216 0.2447760 0.6902309 0.02134884 0.02899630 0.01442554
## 168 168 0.8711338 0.2449167 0.6901520 0.02144511 0.02920371 0.01440488
## 169 169 0.8710861 0.2449973 0.6900785 0.02141829 0.02920969 0.01432771
## 170 170 0.8711177 0.2449500 0.6900418 0.02135538 0.02916873 0.01423370
## 171 171 0.8711211 0.2449541 0.6900218 0.02132538 0.02932554 0.01413091
## 172 172 0.8710780 0.2450230 0.6899941 0.02129128 0.02939646 0.01405897
## 173 173 0.8709928 0.2451605 0.6899367 0.02124310 0.02937252 0.01406856
## 174 174 0.8710301 0.2450961 0.6899956 0.02117073 0.02935160 0.01397037
## 175 175 0.8710716 0.2450239 0.6900741 0.02113136 0.02924623 0.01394193
## 176 176 0.8710630 0.2450432 0.6900440 0.02109010 0.02922176 0.01392667
## 177 177 0.8710327 0.2450848 0.6900352 0.02111557 0.02926948 0.01395574
## 178 178 0.8710528 0.2450638 0.6900243 0.02116066 0.02942807 0.01397555
## 179 179 0.8710596 0.2450525 0.6900270 0.02118822 0.02946012 0.01398807
## 180 180 0.8710546 0.2450577 0.6899895 0.02122769 0.02941479 0.01401486
## 181 181 0.8710621 0.2450610 0.6899539 0.02113441 0.02930182 0.01393759
## 182 182 0.8710273 0.2451195 0.6899044 0.02113192 0.02932570 0.01387505
## 183 183 0.8710678 0.2450663 0.6898991 0.02116056 0.02934898 0.01389266
## 184 184 0.8710276 0.2451175 0.6898601 0.02103778 0.02919606 0.01382968
## 185 185 0.8709995 0.2451600 0.6898525 0.02103351 0.02910620 0.01381127
## 186 186 0.8709884 0.2451744 0.6898756 0.02102556 0.02914508 0.01378116
## 187 187 0.8709916 0.2451660 0.6898806 0.02108584 0.02925044 0.01380207
## 188 188 0.8709931 0.2451716 0.6899296 0.02110156 0.02925602 0.01386441
## 189 189 0.8710116 0.2451511 0.6899808 0.02109192 0.02924510 0.01391869
## 190 190 0.8710250 0.2451236 0.6899950 0.02104918 0.02916591 0.01390334
## 191 191 0.8709487 0.2452523 0.6899603 0.02110662 0.02917305 0.01403939
## 192 192 0.8709544 0.2452412 0.6899522 0.02111542 0.02914872 0.01401714
## 193 193 0.8709757 0.2452073 0.6899711 0.02108689 0.02905452 0.01400590
## 194 194 0.8709095 0.2453121 0.6898963 0.02114777 0.02914047 0.01408294
## 195 195 0.8709330 0.2452625 0.6899183 0.02110893 0.02901316 0.01403361
## 196 196 0.8709160 0.2452925 0.6898779 0.02111789 0.02900687 0.01404222
## 197 197 0.8709516 0.2452424 0.6898881 0.02111298 0.02901593 0.01403159
## 198 198 0.8709218 0.2452872 0.6898667 0.02113950 0.02900133 0.01407111
## 199 199 0.8708782 0.2453475 0.6898381 0.02113509 0.02902172 0.01405424
## 200 200 0.8709196 0.2452859 0.6899149 0.02111217 0.02898765 0.01403278
## 201 201 0.8709730 0.2452037 0.6899384 0.02109812 0.02897965 0.01405802
## 202 202 0.8708995 0.2453109 0.6898949 0.02108571 0.02897273 0.01404901
## 203 203 0.8708754 0.2453524 0.6898682 0.02106880 0.02900676 0.01399838
## 204 204 0.8708616 0.2453799 0.6898729 0.02106382 0.02893318 0.01400573
## 205 205 0.8708489 0.2454034 0.6898480 0.02109830 0.02899896 0.01403535
## 206 206 0.8708463 0.2454086 0.6898147 0.02109298 0.02900501 0.01405162
## 207 207 0.8708843 0.2453488 0.6898349 0.02108793 0.02894228 0.01407714
## 208 208 0.8708767 0.2453638 0.6898140 0.02106154 0.02888536 0.01407658
## 209 209 0.8708811 0.2453599 0.6898047 0.02106125 0.02889934 0.01404389
## 210 210 0.8708825 0.2453493 0.6898012 0.02100837 0.02888897 0.01402877
## 211 211 0.8708911 0.2453340 0.6898066 0.02100218 0.02885073 0.01400499
## 212 212 0.8709106 0.2453006 0.6898119 0.02097700 0.02878977 0.01397276
## 213 213 0.8709249 0.2452777 0.6898214 0.02100077 0.02874207 0.01398078
## 214 214 0.8709303 0.2452699 0.6898206 0.02096002 0.02869285 0.01393806
## 215 215 0.8709433 0.2452467 0.6898224 0.02095978 0.02868184 0.01394823
## 216 216 0.8709181 0.2452826 0.6898032 0.02091984 0.02862750 0.01391265
## 217 217 0.8709383 0.2452499 0.6898019 0.02090191 0.02857891 0.01389143
## 218 218 0.8709594 0.2452213 0.6898122 0.02092089 0.02859895 0.01389152
## 219 219 0.8709504 0.2452379 0.6898225 0.02095061 0.02860147 0.01392855
## 220 220 0.8709595 0.2452215 0.6898295 0.02091165 0.02857674 0.01389363
## 221 221 0.8709620 0.2452193 0.6898415 0.02091774 0.02861461 0.01390884
## 222 222 0.8709319 0.2452631 0.6898199 0.02092204 0.02862018 0.01392612
## 223 223 0.8709544 0.2452302 0.6898368 0.02091255 0.02862773 0.01391731
## 224 224 0.8709725 0.2451994 0.6898613 0.02087188 0.02860715 0.01387450
## 225 225 0.8709869 0.2451785 0.6898824 0.02088212 0.02864416 0.01388276
## 226 226 0.8709852 0.2451818 0.6898890 0.02087977 0.02866299 0.01387216
## 227 227 0.8709797 0.2451895 0.6898882 0.02086446 0.02866466 0.01385896
## 228 228 0.8709643 0.2452123 0.6898701 0.02087165 0.02869113 0.01385553
## 229 229 0.8709644 0.2452103 0.6898638 0.02085810 0.02867718 0.01384820
## 230 230 0.8709578 0.2452244 0.6898649 0.02087034 0.02869888 0.01386252
## 231 231 0.8709710 0.2452039 0.6898742 0.02086966 0.02867474 0.01386523
## 232 232 0.8709715 0.2452026 0.6898689 0.02086836 0.02866868 0.01385658
## 233 233 0.8709690 0.2452062 0.6898742 0.02086723 0.02865404 0.01385623
## 234 234 0.8709601 0.2452207 0.6898732 0.02087161 0.02865150 0.01385151
## 235 235 0.8709550 0.2452287 0.6898716 0.02086803 0.02864109 0.01384866
## 236 236 0.8709448 0.2452437 0.6898626 0.02086830 0.02862462 0.01385545
## 237 237 0.8709466 0.2452403 0.6898668 0.02087086 0.02862439 0.01386168
## 238 238 0.8709469 0.2452393 0.6898647 0.02086630 0.02861955 0.01386117
## 239 239 0.8709465 0.2452396 0.6898656 0.02086696 0.02862015 0.01386082
## 240 240 0.8709482 0.2452370 0.6898676 0.02086412 0.02861708 0.01385710
## nvmax
## 13 13## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## -3.832583e+00 -1.336998e-03 3.502871e-01 1.316519e-02 1.026206e-01
## x10 x11 x16 x17 x21
## 3.532314e-02 6.547159e+06 2.676086e-02 3.808197e-02 3.363089e-03
## stat14 stat98 stat110 sqrt.x18
## -2.807359e-02 1.008003e-01 -9.721343e-02 7.689960e-01Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.615548 -0.383649 0.009481 -0.010402 0.359098 1.565338
## [1] "leapBackward Test MSE: 0.707385129532587"
Backward Elimination with CV (w/ filtered train)
Train
if (algo.backward.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "leapBackward"
,feature.names = feature.names)
model.backward = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 17 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.8409023 0.1577218 0.6879777 0.01268808 0.03072104 0.01129038
## 2 2 0.8033473 0.2319526 0.6608168 0.01175259 0.03593694 0.01051046
## 3 3 0.7837206 0.2693743 0.6423414 0.01806620 0.04565986 0.01381834
## 4 4 0.7609968 0.3105987 0.6196852 0.01993332 0.04660860 0.01470158
## 5 5 0.7484550 0.3326744 0.6098049 0.01972914 0.04767659 0.01512932
## 6 6 0.7445668 0.3396824 0.6073199 0.01970809 0.04865528 0.01514497
## 7 7 0.7443466 0.3399573 0.6072336 0.01890122 0.04736629 0.01403613
## 8 8 0.7424917 0.3430195 0.6063003 0.01804282 0.04515576 0.01382522
## 9 9 0.7401689 0.3468840 0.6048158 0.01731572 0.04337614 0.01408610
## 10 10 0.7365026 0.3532011 0.6026346 0.01616534 0.04154642 0.01295357
## 11 11 0.7376458 0.3512336 0.6032208 0.01601369 0.04073140 0.01253736
## 12 12 0.7384233 0.3498306 0.6035382 0.01612959 0.04097902 0.01250952
## 13 13 0.7379167 0.3507470 0.6030363 0.01664144 0.04048830 0.01313803
## 14 14 0.7366512 0.3529638 0.6023852 0.01627358 0.03987887 0.01271745
## 15 15 0.7363301 0.3534808 0.6018782 0.01553689 0.03820284 0.01210340
## 16 16 0.7366222 0.3530071 0.6022254 0.01480923 0.03657084 0.01189792
## 17 17 0.7359792 0.3540951 0.6018725 0.01419055 0.03605513 0.01155377
## 18 18 0.7361176 0.3538452 0.6018325 0.01415101 0.03464478 0.01156232
## 19 19 0.7370058 0.3523217 0.6022445 0.01442206 0.03474940 0.01227024
## 20 20 0.7373494 0.3517097 0.6024701 0.01386563 0.03336486 0.01206019
## 21 21 0.7372872 0.3518576 0.6025976 0.01408742 0.03349182 0.01227980
## 22 22 0.7368578 0.3526703 0.6017602 0.01376350 0.03334901 0.01211062
## 23 23 0.7367504 0.3529462 0.6015381 0.01458134 0.03387280 0.01297660
## 24 24 0.7371404 0.3523316 0.6018426 0.01444733 0.03421394 0.01288883
## 25 25 0.7372072 0.3523172 0.6018999 0.01440715 0.03406879 0.01271822
## 26 26 0.7373271 0.3521229 0.6018398 0.01429050 0.03351260 0.01278040
## 27 27 0.7372127 0.3523470 0.6017150 0.01458782 0.03434620 0.01272492
## 28 28 0.7372755 0.3522307 0.6016442 0.01463069 0.03437772 0.01264547
## 29 29 0.7375749 0.3517467 0.6021127 0.01488587 0.03398231 0.01289704
## 30 30 0.7375227 0.3518716 0.6023500 0.01459128 0.03365574 0.01286878
## 31 31 0.7377705 0.3514657 0.6024597 0.01422051 0.03324631 0.01279901
## 32 32 0.7383128 0.3506111 0.6028133 0.01454079 0.03376370 0.01322200
## 33 33 0.7382718 0.3507042 0.6027945 0.01471495 0.03408913 0.01361802
## 34 34 0.7377549 0.3515500 0.6023653 0.01482602 0.03350321 0.01345569
## 35 35 0.7379240 0.3512618 0.6025720 0.01491329 0.03457184 0.01372266
## 36 36 0.7381104 0.3509596 0.6024181 0.01508856 0.03480513 0.01389831
## 37 37 0.7381141 0.3509782 0.6025277 0.01487787 0.03412089 0.01352841
## 38 38 0.7378770 0.3513823 0.6025274 0.01454885 0.03356583 0.01325738
## 39 39 0.7377048 0.3516502 0.6023915 0.01459231 0.03387719 0.01291884
## 40 40 0.7375929 0.3518995 0.6023054 0.01471345 0.03372080 0.01275177
## 41 41 0.7378914 0.3514446 0.6024821 0.01485094 0.03338336 0.01295691
## 42 42 0.7378371 0.3515504 0.6023102 0.01506818 0.03345314 0.01271449
## 43 43 0.7379491 0.3513231 0.6021885 0.01479952 0.03248226 0.01213840
## 44 44 0.7374019 0.3522785 0.6020382 0.01488189 0.03282134 0.01251166
## 45 45 0.7376586 0.3518105 0.6022347 0.01460333 0.03210624 0.01254027
## 46 46 0.7375983 0.3519461 0.6022689 0.01456510 0.03182410 0.01265723
## 47 47 0.7373981 0.3523003 0.6020758 0.01489169 0.03232813 0.01264166
## 48 48 0.7376898 0.3518168 0.6024962 0.01484189 0.03169575 0.01263589
## 49 49 0.7377216 0.3517911 0.6024667 0.01512100 0.03191734 0.01289085
## 50 50 0.7376040 0.3520472 0.6025506 0.01523184 0.03201908 0.01285872
## 51 51 0.7372365 0.3526887 0.6023352 0.01540784 0.03284209 0.01294571
## 52 52 0.7371219 0.3528779 0.6024201 0.01576571 0.03312795 0.01316877
## 53 53 0.7370394 0.3530361 0.6024870 0.01562419 0.03245547 0.01335903
## 54 54 0.7372152 0.3527328 0.6025736 0.01547709 0.03205097 0.01356658
## 55 55 0.7372708 0.3526065 0.6025575 0.01568696 0.03241263 0.01384578
## 56 56 0.7372332 0.3526753 0.6023962 0.01578725 0.03251495 0.01381077
## 57 57 0.7376137 0.3520636 0.6025758 0.01583334 0.03237593 0.01362276
## 58 58 0.7374795 0.3522699 0.6023492 0.01605502 0.03298030 0.01365373
## 59 59 0.7377744 0.3518425 0.6024588 0.01643782 0.03310926 0.01378051
## 60 60 0.7380791 0.3513853 0.6026282 0.01645696 0.03329783 0.01409137
## 61 61 0.7381397 0.3513384 0.6027551 0.01664938 0.03344053 0.01419329
## 62 62 0.7382508 0.3511727 0.6029704 0.01658724 0.03363007 0.01432887
## 63 63 0.7382576 0.3512027 0.6028737 0.01629372 0.03356103 0.01417732
## 64 64 0.7383746 0.3509849 0.6030757 0.01605154 0.03315082 0.01376910
## 65 65 0.7387327 0.3504177 0.6034507 0.01618137 0.03326129 0.01396999
## 66 66 0.7390461 0.3499040 0.6037078 0.01638624 0.03342893 0.01410269
## 67 67 0.7385045 0.3508627 0.6031545 0.01672898 0.03421736 0.01432526
## 68 68 0.7385858 0.3507369 0.6030607 0.01658705 0.03388235 0.01437774
## 69 69 0.7388391 0.3503356 0.6031353 0.01644201 0.03368935 0.01412372
## 70 70 0.7387980 0.3503680 0.6032025 0.01629353 0.03391067 0.01413948
## 71 71 0.7389873 0.3500915 0.6032766 0.01635571 0.03417024 0.01408100
## 72 72 0.7391037 0.3499219 0.6035064 0.01609167 0.03384324 0.01384300
## 73 73 0.7390076 0.3501041 0.6033828 0.01635003 0.03443825 0.01409387
## 74 74 0.7390843 0.3499814 0.6034307 0.01595343 0.03295164 0.01364662
## 75 75 0.7388027 0.3504508 0.6032022 0.01579688 0.03262158 0.01348111
## 76 76 0.7390225 0.3501074 0.6034206 0.01590821 0.03293398 0.01349360
## 77 77 0.7389030 0.3502661 0.6034050 0.01571751 0.03277041 0.01349978
## 78 78 0.7391950 0.3497614 0.6036815 0.01536125 0.03222841 0.01334885
## 79 79 0.7392425 0.3497024 0.6037829 0.01522320 0.03201525 0.01352886
## 80 80 0.7394061 0.3494400 0.6038718 0.01550948 0.03273047 0.01405218
## 81 81 0.7395108 0.3492558 0.6039439 0.01524335 0.03254848 0.01384311
## 82 82 0.7395085 0.3492685 0.6039023 0.01514255 0.03225488 0.01365647
## 83 83 0.7395349 0.3492309 0.6037019 0.01504171 0.03239066 0.01359883
## 84 84 0.7397027 0.3489361 0.6040221 0.01512988 0.03231981 0.01373316
## 85 85 0.7400295 0.3484032 0.6042821 0.01500098 0.03236451 0.01358936
## 86 86 0.7398662 0.3486751 0.6041728 0.01502200 0.03241720 0.01354784
## 87 87 0.7397505 0.3488814 0.6041123 0.01485928 0.03183120 0.01317509
## 88 88 0.7399670 0.3485393 0.6042951 0.01509531 0.03222962 0.01318533
## 89 89 0.7400661 0.3483583 0.6043356 0.01478480 0.03182382 0.01315734
## 90 90 0.7403943 0.3478274 0.6045738 0.01455955 0.03141622 0.01284217
## 91 91 0.7401238 0.3482523 0.6043072 0.01459990 0.03140993 0.01286387
## 92 92 0.7400457 0.3484523 0.6043260 0.01480801 0.03152765 0.01311500
## 93 93 0.7404453 0.3478160 0.6046014 0.01478363 0.03132169 0.01312466
## 94 94 0.7403691 0.3479306 0.6048070 0.01484601 0.03168078 0.01324328
## 95 95 0.7406790 0.3474266 0.6050102 0.01501495 0.03153065 0.01338118
## 96 96 0.7406927 0.3474062 0.6050747 0.01493761 0.03158714 0.01332951
## 97 97 0.7409219 0.3470317 0.6052091 0.01468682 0.03153482 0.01316262
## 98 98 0.7408168 0.3472237 0.6050968 0.01484045 0.03179378 0.01317662
## 99 99 0.7405184 0.3477307 0.6048749 0.01456939 0.03177738 0.01275284
## 100 100 0.7404346 0.3478943 0.6048496 0.01450319 0.03184880 0.01273761
## 101 101 0.7402881 0.3481396 0.6048540 0.01453947 0.03191112 0.01268602
## 102 102 0.7403832 0.3479879 0.6048800 0.01453767 0.03202345 0.01257459
## 103 103 0.7403688 0.3480146 0.6049395 0.01464919 0.03183649 0.01270745
## 104 104 0.7403124 0.3480989 0.6049820 0.01472580 0.03204819 0.01281036
## 105 105 0.7402172 0.3482438 0.6049548 0.01458002 0.03190200 0.01267844
## 106 106 0.7402993 0.3480933 0.6051075 0.01501857 0.03242247 0.01298598
## 107 107 0.7401874 0.3482979 0.6050092 0.01496628 0.03244739 0.01280919
## 108 108 0.7402630 0.3481861 0.6049969 0.01465175 0.03227564 0.01244856
## 109 109 0.7404468 0.3478790 0.6051420 0.01440410 0.03217258 0.01219586
## 110 110 0.7406564 0.3475380 0.6053295 0.01472767 0.03273813 0.01256121
## 111 111 0.7407610 0.3473714 0.6054144 0.01467090 0.03248219 0.01259669
## 112 112 0.7407580 0.3473811 0.6053564 0.01443215 0.03206156 0.01238517
## 113 113 0.7407080 0.3474429 0.6053652 0.01420734 0.03208182 0.01224972
## 114 114 0.7406229 0.3475908 0.6053490 0.01437824 0.03210315 0.01235196
## 115 115 0.7405596 0.3477036 0.6052809 0.01439425 0.03219187 0.01231626
## 116 116 0.7406679 0.3475347 0.6051848 0.01418109 0.03191825 0.01223038
## 117 117 0.7404972 0.3478340 0.6050197 0.01418217 0.03168933 0.01235486
## 118 118 0.7403845 0.3480213 0.6049690 0.01414636 0.03162111 0.01232143
## 119 119 0.7402303 0.3482506 0.6047466 0.01430091 0.03165942 0.01260343
## 120 120 0.7401861 0.3483384 0.6047335 0.01428693 0.03152260 0.01262279
## 121 121 0.7402103 0.3483155 0.6046222 0.01448635 0.03188687 0.01276138
## 122 122 0.7403483 0.3480836 0.6048353 0.01434917 0.03179222 0.01282640
## 123 123 0.7402045 0.3483202 0.6047277 0.01446786 0.03190504 0.01311068
## 124 124 0.7403518 0.3480793 0.6049054 0.01451475 0.03209309 0.01322338
## 125 125 0.7405213 0.3477938 0.6049572 0.01450236 0.03182728 0.01310405
## 126 126 0.7406014 0.3476707 0.6050529 0.01431562 0.03145696 0.01292992
## 127 127 0.7405324 0.3477840 0.6049756 0.01432301 0.03117267 0.01290428
## 128 128 0.7404835 0.3478769 0.6049493 0.01443204 0.03120109 0.01304867
## 129 129 0.7407094 0.3475097 0.6051054 0.01457132 0.03169228 0.01323954
## 130 130 0.7407116 0.3475143 0.6049965 0.01469228 0.03185349 0.01327648
## 131 131 0.7408535 0.3473061 0.6052189 0.01469072 0.03166311 0.01319833
## 132 132 0.7410539 0.3469893 0.6053379 0.01473804 0.03155754 0.01322387
## 133 133 0.7411810 0.3467911 0.6055590 0.01463346 0.03130477 0.01314360
## 134 134 0.7414184 0.3464083 0.6057505 0.01459540 0.03142496 0.01299147
## 135 135 0.7415048 0.3462832 0.6057333 0.01456210 0.03138466 0.01289830
## 136 136 0.7415867 0.3461395 0.6057500 0.01447461 0.03136812 0.01282086
## 137 137 0.7415149 0.3462774 0.6057140 0.01456202 0.03141484 0.01293619
## 138 138 0.7416134 0.3461474 0.6056903 0.01477512 0.03171859 0.01317331
## 139 139 0.7417650 0.3458963 0.6057407 0.01491138 0.03169137 0.01321146
## 140 140 0.7417896 0.3458590 0.6057133 0.01502446 0.03167373 0.01331165
## 141 141 0.7416891 0.3460490 0.6055564 0.01508210 0.03148584 0.01335452
## 142 142 0.7416292 0.3461382 0.6055340 0.01503479 0.03119411 0.01327337
## 143 143 0.7417116 0.3460255 0.6055365 0.01520288 0.03147391 0.01337913
## 144 144 0.7417769 0.3459220 0.6056472 0.01529707 0.03157738 0.01339249
## 145 145 0.7419946 0.3455530 0.6058434 0.01528981 0.03146605 0.01333651
## 146 146 0.7421110 0.3453583 0.6058797 0.01530408 0.03166371 0.01347253
## 147 147 0.7423485 0.3449507 0.6060911 0.01529595 0.03161342 0.01351053
## 148 148 0.7423636 0.3449139 0.6059721 0.01521092 0.03146740 0.01341186
## 149 149 0.7425405 0.3446211 0.6060859 0.01509343 0.03129561 0.01342711
## 150 150 0.7424150 0.3448364 0.6060554 0.01501784 0.03127619 0.01334663
## 151 151 0.7424141 0.3448346 0.6060622 0.01506357 0.03133307 0.01333572
## 152 152 0.7424888 0.3447051 0.6061353 0.01506561 0.03133366 0.01336135
## 153 153 0.7425556 0.3445889 0.6061403 0.01512377 0.03125195 0.01341137
## 154 154 0.7426530 0.3444160 0.6061866 0.01506126 0.03125537 0.01341753
## 155 155 0.7427099 0.3443349 0.6061559 0.01509819 0.03111321 0.01350801
## 156 156 0.7427972 0.3442023 0.6061571 0.01510239 0.03120699 0.01352352
## 157 157 0.7429206 0.3439946 0.6062349 0.01505108 0.03118317 0.01336463
## 158 158 0.7430134 0.3438544 0.6062534 0.01486129 0.03098752 0.01316730
## 159 159 0.7430210 0.3438554 0.6062399 0.01486083 0.03113717 0.01319621
## 160 160 0.7429634 0.3439434 0.6061887 0.01487255 0.03128419 0.01327055
## 161 161 0.7429806 0.3439221 0.6062216 0.01490870 0.03141110 0.01329477
## 162 162 0.7429664 0.3439669 0.6062678 0.01496166 0.03133529 0.01339820
## 163 163 0.7429365 0.3440385 0.6061456 0.01496788 0.03136039 0.01339229
## 164 164 0.7429257 0.3440758 0.6060603 0.01504588 0.03141407 0.01344777
## 165 165 0.7429236 0.3440784 0.6060079 0.01499143 0.03135895 0.01342858
## 166 166 0.7430017 0.3439519 0.6060545 0.01506918 0.03141121 0.01341857
## 167 167 0.7430000 0.3439638 0.6060575 0.01519273 0.03163045 0.01359427
## 168 168 0.7429947 0.3439801 0.6060982 0.01512208 0.03156283 0.01355600
## 169 169 0.7430206 0.3439554 0.6060677 0.01516084 0.03151923 0.01355900
## 170 170 0.7431220 0.3437971 0.6061193 0.01521663 0.03164527 0.01355039
## 171 171 0.7431651 0.3437330 0.6061491 0.01531951 0.03188253 0.01358671
## 172 172 0.7431836 0.3437089 0.6061519 0.01526135 0.03170844 0.01356894
## 173 173 0.7431549 0.3437595 0.6061103 0.01527614 0.03167681 0.01359743
## 174 174 0.7432130 0.3436674 0.6061076 0.01523227 0.03168428 0.01353175
## 175 175 0.7432499 0.3436157 0.6061330 0.01519459 0.03164073 0.01344587
## 176 176 0.7432048 0.3436708 0.6060405 0.01503029 0.03137983 0.01336362
## 177 177 0.7432390 0.3436147 0.6060741 0.01505200 0.03140119 0.01339209
## 178 178 0.7431913 0.3436909 0.6060696 0.01507066 0.03146759 0.01334215
## 179 179 0.7432746 0.3435630 0.6061199 0.01507136 0.03141377 0.01333228
## 180 180 0.7432253 0.3436496 0.6060880 0.01512654 0.03147772 0.01335056
## 181 181 0.7432560 0.3435998 0.6061304 0.01527626 0.03167757 0.01348495
## 182 182 0.7433543 0.3434354 0.6062023 0.01527211 0.03169557 0.01349290
## 183 183 0.7434614 0.3432801 0.6063157 0.01539036 0.03167976 0.01356371
## 184 184 0.7435854 0.3430769 0.6064243 0.01544842 0.03178597 0.01361645
## 185 185 0.7436355 0.3430069 0.6064958 0.01550938 0.03179894 0.01362611
## 186 186 0.7436124 0.3430396 0.6064603 0.01555809 0.03181629 0.01363334
## 187 187 0.7435689 0.3431125 0.6064102 0.01562710 0.03189475 0.01368449
## 188 188 0.7436633 0.3429568 0.6064664 0.01554611 0.03194759 0.01358226
## 189 189 0.7436492 0.3429911 0.6064843 0.01554664 0.03196917 0.01353404
## 190 190 0.7436669 0.3429598 0.6065072 0.01555446 0.03204030 0.01350482
## 191 191 0.7436279 0.3430136 0.6064554 0.01538995 0.03182775 0.01336307
## 192 192 0.7436267 0.3430279 0.6064400 0.01537315 0.03175541 0.01335028
## 193 193 0.7436532 0.3429783 0.6064858 0.01535157 0.03166984 0.01331418
## 194 194 0.7436417 0.3429944 0.6064703 0.01532142 0.03169376 0.01329805
## 195 195 0.7436183 0.3430320 0.6064395 0.01530193 0.03163488 0.01331801
## 196 196 0.7435462 0.3431481 0.6063682 0.01523332 0.03157015 0.01331371
## 197 197 0.7435638 0.3431212 0.6064025 0.01520408 0.03152700 0.01325910
## 198 198 0.7435758 0.3431076 0.6064145 0.01525276 0.03160789 0.01330619
## 199 199 0.7435970 0.3430777 0.6064297 0.01524740 0.03151943 0.01327761
## 200 200 0.7435723 0.3431259 0.6063866 0.01535008 0.03173058 0.01338095
## 201 201 0.7436250 0.3430418 0.6064051 0.01535411 0.03175655 0.01338200
## 202 202 0.7436100 0.3430681 0.6063696 0.01537196 0.03181173 0.01341399
## 203 203 0.7436129 0.3430644 0.6063610 0.01532804 0.03179156 0.01336434
## 204 204 0.7436245 0.3430470 0.6064024 0.01538031 0.03181569 0.01343854
## 205 205 0.7436023 0.3430820 0.6063737 0.01535258 0.03186156 0.01338926
## 206 206 0.7436521 0.3430004 0.6064292 0.01535949 0.03193848 0.01339256
## 207 207 0.7436801 0.3429593 0.6064461 0.01537046 0.03196902 0.01340616
## 208 208 0.7436931 0.3429467 0.6064430 0.01539443 0.03199275 0.01339819
## 209 209 0.7437028 0.3429359 0.6064464 0.01540078 0.03193750 0.01339788
## 210 210 0.7437088 0.3429251 0.6064454 0.01535859 0.03192137 0.01339388
## 211 211 0.7437118 0.3429198 0.6064465 0.01538974 0.03192214 0.01340304
## 212 212 0.7437175 0.3429070 0.6064385 0.01537155 0.03190504 0.01337462
## 213 213 0.7437175 0.3429068 0.6064143 0.01535436 0.03184461 0.01335906
## 214 214 0.7436867 0.3429525 0.6063931 0.01534535 0.03181961 0.01333518
## 215 215 0.7436678 0.3429802 0.6063712 0.01532964 0.03176031 0.01332230
## 216 216 0.7436439 0.3430165 0.6063530 0.01528882 0.03171237 0.01329207
## 217 217 0.7436677 0.3429788 0.6063870 0.01530666 0.03176973 0.01329694
## 218 218 0.7437034 0.3429187 0.6064184 0.01532022 0.03182433 0.01331454
## 219 219 0.7437012 0.3429249 0.6064269 0.01531953 0.03183132 0.01332384
## 220 220 0.7436988 0.3429267 0.6064309 0.01531341 0.03185490 0.01330329
## 221 221 0.7437093 0.3429110 0.6064433 0.01535698 0.03193747 0.01335169
## 222 222 0.7436958 0.3429315 0.6064429 0.01536589 0.03196301 0.01337918
## 223 223 0.7436896 0.3429430 0.6064373 0.01537708 0.03199155 0.01339421
## 224 224 0.7436885 0.3429466 0.6064327 0.01536950 0.03198249 0.01338616
## 225 225 0.7436762 0.3429667 0.6064264 0.01534809 0.03197904 0.01335714
## 226 226 0.7436865 0.3429481 0.6064388 0.01536621 0.03202983 0.01335675
## 227 227 0.7437125 0.3429066 0.6064591 0.01536721 0.03201308 0.01336770
## 228 228 0.7437030 0.3429207 0.6064592 0.01535874 0.03200725 0.01336021
## 229 229 0.7437106 0.3429066 0.6064647 0.01535667 0.03200362 0.01335641
## 230 230 0.7437098 0.3429080 0.6064691 0.01535732 0.03201594 0.01334940
## 231 231 0.7437054 0.3429144 0.6064655 0.01536698 0.03202970 0.01336248
## 232 232 0.7437061 0.3429145 0.6064598 0.01537330 0.03203832 0.01335318
## 233 233 0.7437095 0.3429094 0.6064650 0.01538046 0.03202637 0.01336097
## 234 234 0.7437068 0.3429137 0.6064687 0.01537520 0.03201916 0.01336015
## 235 235 0.7437111 0.3429055 0.6064710 0.01538018 0.03202291 0.01336781
## 236 236 0.7437075 0.3429119 0.6064647 0.01537818 0.03201369 0.01336517
## 237 237 0.7437101 0.3429086 0.6064644 0.01538230 0.03202068 0.01336713
## 238 238 0.7437119 0.3429055 0.6064625 0.01538549 0.03202128 0.01336905
## 239 239 0.7437121 0.3429051 0.6064628 0.01538583 0.03202209 0.01337002
## 240 240 0.7437119 0.3429053 0.6064635 0.01538568 0.03202256 0.01336963
## nvmax
## 17 17## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## -4.116119e+00 -1.619117e-03 3.779949e-01 1.292227e-02 1.051306e-01
## x10 x11 x13 x16 x17
## 4.277716e-02 6.539776e+06 5.066423e-03 2.612787e-02 4.365707e-02
## x21 stat4 stat14 stat41 stat98
## 3.788257e-03 -1.817541e-02 -3.261225e-02 -1.897833e-02 1.064435e-01
## stat100 stat110 sqrt.x18
## 1.807879e-02 -9.987636e-02 7.991268e-01Test
if (algo.backward.caret == TRUE){
test.model(model.backward, data.test
,method = 'leapBackward',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.74771 -0.47381 -0.04996 -0.07002 0.32217 1.61661
## [1] "leapBackward Test MSE: 0.713899047411159"
Stepwise Selection (w/ full train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise = step(model.null, scope=list(upper=model.full), data = data.train, direction="both", trace = 0)
print(summary(model.stepwise))
#saveRDS(model.stepwise,file = "model_stepwise.rds")
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise, data.train)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise, data.test, "Stepwise Selection")
}Stepwise Selection (w/ filtered train)
Train
if (algo.stepwise == TRUE){
t1 = Sys.time()
model.stepwise2 = step(model.null2, scope=list(upper=model.full2), data = data.train2, direction="both", trace = 0)
print(summary(model.stepwise2))
#saveRDS(model.forward,file = "model_stepwise.rds")
t2 = Sys.time()
print (paste("Time taken for Stepwise Selection: ",t2-t1, sep = ""))
plot.diagnostics(model.stepwise2, data.train2)
}Test
if (algo.stepwise == TRUE){
test.model(model.stepwise2, data.test, "Stepwise Selection (2)")
}Stepwise Selection with CV (w/ full train)
Train
if (algo.stepwise.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "leapSeq"
,feature.names = feature.names)
model.stepwise = returned$model
id = returned$id
}## Aggregating results
## Selecting tuning parameters
## Fitting nvmax = 13 on full training set
## nvmax RMSE Rsquared MAE RMSESD RsquaredSD MAESD
## 1 1 0.9365643 0.1236737 0.7536160 0.01668298 0.01993896 0.01279423
## 2 2 0.9079494 0.1770387 0.7298664 0.01750665 0.02892992 0.01357292
## 3 3 0.8944193 0.2008249 0.7155952 0.01901631 0.02919065 0.01320105
## 4 4 0.8742691 0.2363913 0.6937420 0.02185141 0.03097169 0.01484363
## 5 5 0.8638186 0.2544505 0.6856338 0.02338283 0.03597771 0.01555964
## 6 6 0.8614669 0.2584423 0.6838031 0.02330853 0.03655409 0.01630994
## 7 7 0.8616264 0.2581201 0.6838827 0.02252992 0.03576400 0.01543925
## 8 8 0.8606372 0.2598909 0.6832436 0.02346111 0.03617819 0.01574605
## 9 9 0.8589201 0.2629713 0.6817973 0.02303138 0.03595457 0.01457108
## 10 10 0.8560569 0.2677599 0.6802123 0.02220196 0.03458445 0.01366388
## 11 11 0.8566254 0.2668271 0.6804815 0.02260781 0.03462453 0.01470736
## 12 12 0.8560935 0.2677508 0.6801040 0.02254458 0.03351414 0.01464169
## 13 13 0.8555747 0.2686228 0.6800973 0.02206305 0.03335563 0.01510989
## 14 14 0.8684996 0.2458145 0.6912694 0.04555876 0.07697800 0.03766811
## 15 15 0.8564294 0.2671722 0.6809500 0.02114252 0.03193376 0.01400192
## 16 16 0.8572779 0.2657165 0.6813558 0.02112224 0.03148819 0.01412545
## 17 17 0.8582069 0.2641794 0.6810568 0.02120382 0.03072687 0.01417740
## 18 18 0.8683081 0.2457771 0.6890683 0.03781980 0.06997428 0.02875000
## 19 19 0.8589868 0.2629528 0.6816334 0.02177558 0.03061139 0.01507559
## 20 20 0.8594587 0.2621429 0.6820007 0.02169319 0.03064491 0.01488072
## 21 21 0.8678498 0.2483197 0.6886636 0.03595766 0.04640848 0.02849371
## 22 22 0.8708501 0.2419670 0.6916296 0.04217455 0.07105227 0.03449019
## 23 23 0.8810467 0.2234093 0.6988261 0.04534472 0.07411202 0.03735346
## 24 24 0.8600881 0.2611761 0.6823737 0.02007091 0.02934298 0.01373595
## 25 25 0.8597106 0.2618264 0.6820676 0.01977088 0.02939082 0.01361317
## 26 26 0.8597122 0.2618222 0.6821121 0.01991373 0.02947959 0.01351560
## 27 27 0.8602658 0.2609066 0.6828477 0.01955309 0.02872751 0.01344418
## 28 28 0.8606623 0.2602611 0.6834735 0.01934106 0.02845230 0.01325420
## 29 29 0.8700300 0.2427569 0.6915041 0.02582542 0.05922596 0.02239276
## 30 30 0.8607544 0.2601400 0.6836420 0.01890604 0.02802257 0.01297868
## 31 31 0.8822959 0.2215009 0.7010596 0.04860779 0.08772134 0.03984491
## 32 32 0.9028842 0.1848311 0.7178647 0.06526738 0.09863975 0.05341226
## 33 33 0.8699817 0.2427809 0.6901422 0.03509189 0.06834113 0.02579762
## 34 34 0.8619047 0.2582813 0.6840148 0.01916073 0.02949779 0.01381832
## 35 35 0.8733213 0.2379619 0.6927647 0.04238067 0.06708812 0.03363947
## 36 36 0.8627138 0.2569905 0.6846949 0.01920017 0.02907708 0.01353399
## 37 37 0.8627289 0.2569871 0.6847022 0.01946511 0.02971603 0.01372192
## 38 38 0.8701930 0.2425128 0.6899009 0.03433132 0.06696438 0.02494788
## 39 39 0.8624480 0.2574873 0.6844157 0.01956892 0.02998905 0.01393425
## 40 40 0.8797562 0.2251616 0.6985789 0.04317398 0.08106969 0.03073313
## 41 41 0.8631472 0.2563417 0.6845997 0.01931524 0.02937825 0.01399582
## 42 42 0.8821562 0.2214747 0.6996974 0.04867200 0.08943125 0.03900645
## 43 43 0.8741159 0.2368415 0.6925741 0.04320596 0.06826614 0.03449859
## 44 44 0.8745547 0.2362301 0.6941923 0.04142863 0.07041583 0.03421045
## 45 45 0.8819326 0.2214796 0.6994800 0.02552627 0.07106821 0.02272890
## 46 46 0.9026960 0.1859372 0.7164704 0.04777782 0.08844200 0.04028648
## 47 47 0.8701520 0.2430572 0.6907379 0.03550407 0.06053715 0.02763191
## 48 48 0.8641921 0.2548261 0.6851247 0.01966986 0.02915284 0.01396755
## 49 49 0.8645180 0.2543106 0.6853467 0.01963492 0.02934337 0.01397718
## 50 50 0.8819576 0.2235049 0.6989654 0.04258227 0.06935197 0.03508431
## 51 51 0.8731167 0.2381544 0.6922569 0.02466306 0.05692725 0.02151898
## 52 52 0.8762601 0.2336580 0.6941138 0.04306383 0.06734889 0.03443684
## 53 53 0.8798719 0.2254673 0.6984527 0.04416932 0.08089270 0.03501418
## 54 54 0.8652586 0.2532166 0.6852904 0.01969358 0.02942862 0.01408760
## 55 55 0.8652025 0.2533017 0.6852252 0.01977879 0.02915355 0.01420397
## 56 56 0.8652781 0.2532059 0.6852678 0.01970730 0.02956590 0.01403179
## 57 57 0.8745841 0.2362678 0.6937008 0.03451621 0.05839301 0.02465673
## 58 58 0.8863463 0.2169775 0.7012670 0.04821335 0.06047005 0.03984165
## 59 59 0.8657470 0.2524974 0.6854787 0.01972586 0.02973751 0.01403003
## 60 60 0.8654432 0.2530639 0.6854319 0.02007355 0.03051382 0.01396901
## 61 61 0.8932155 0.2021862 0.7089150 0.04659690 0.09315985 0.04020249
## 62 62 0.8652109 0.2534149 0.6848095 0.01984374 0.03025510 0.01383974
## 63 63 0.8715784 0.2409364 0.6909191 0.03629375 0.06193559 0.02747638
## 64 64 0.8760296 0.2336382 0.6933162 0.02566840 0.05958077 0.01904316
## 65 65 0.8880385 0.2125886 0.7021296 0.04299140 0.07926482 0.03501457
## 66 66 0.8725339 0.2395036 0.6916220 0.03634364 0.06172763 0.02743279
## 67 67 0.8834021 0.2214880 0.7001810 0.04202842 0.06328680 0.03233084
## 68 68 0.8668116 0.2509942 0.6860174 0.02003041 0.02942325 0.01394937
## 69 69 0.8751975 0.2355059 0.6931327 0.03478228 0.06442927 0.02695087
## 70 70 0.8666732 0.2512500 0.6862264 0.02021154 0.02917964 0.01377957
## 71 71 0.9060678 0.1811125 0.7179772 0.05270345 0.08156244 0.04574893
## 72 72 0.8669202 0.2508642 0.6861849 0.02022137 0.02891208 0.01394547
## 73 73 0.8675436 0.2498871 0.6868014 0.02074609 0.02877446 0.01429179
## 74 74 0.8733473 0.2383365 0.6926002 0.03615040 0.06062940 0.02743200
## 75 75 0.8960918 0.1994615 0.7090151 0.05361269 0.07794844 0.04366084
## 76 76 0.9024684 0.1852846 0.7163689 0.05230750 0.09356539 0.04153517
## 77 77 0.8669084 0.2509829 0.6864569 0.02065770 0.02835492 0.01421158
## 78 78 0.8776902 0.2313026 0.6946336 0.02513841 0.05748066 0.01774298
## 79 79 0.8791868 0.2298142 0.6954161 0.04316382 0.05748931 0.03351265
## 80 80 0.8757350 0.2358449 0.6933919 0.03425949 0.04489868 0.02733257
## 81 81 0.8765512 0.2330180 0.6941004 0.02445984 0.05681275 0.02112592
## 82 82 0.8765853 0.2335071 0.6954750 0.03456584 0.05725502 0.02575949
## 83 83 0.8680133 0.2493031 0.6874608 0.02049275 0.02866357 0.01389431
## 84 84 0.8867676 0.2155631 0.7021050 0.03523085 0.06312158 0.02776164
## 85 85 0.8874227 0.2143836 0.7052181 0.04758269 0.08050568 0.03787617
## 86 86 0.8684358 0.2486752 0.6878719 0.02031437 0.02834568 0.01402231
## 87 87 0.8862128 0.2155965 0.7019380 0.03530312 0.07689518 0.02948677
## 88 88 0.8876323 0.2148273 0.7040960 0.04803901 0.07366866 0.04013627
## 89 89 0.8890348 0.2128303 0.7033102 0.04926284 0.06265417 0.03916855
## 90 90 0.8791539 0.2297972 0.6971900 0.04174764 0.06906331 0.03471808
## 91 91 0.8881358 0.2121384 0.7027069 0.02402746 0.06971902 0.02008159
## 92 92 0.8961761 0.1980263 0.7110722 0.05397855 0.09997081 0.04344317
## 93 93 0.8767937 0.2328224 0.6944043 0.03546367 0.06640248 0.02600166
## 94 94 0.8896919 0.2110558 0.7050542 0.04943229 0.07173124 0.03794960
## 95 95 0.8893312 0.2115198 0.7048082 0.04966344 0.07924278 0.03827840
## 96 96 0.8693403 0.2473081 0.6885215 0.02090901 0.02951080 0.01422812
## 97 97 0.8881952 0.2132589 0.7031636 0.03500766 0.06315916 0.02769629
## 98 98 0.8694349 0.2471385 0.6885569 0.02082652 0.02915339 0.01456965
## 99 99 0.8779681 0.2313865 0.6965128 0.03466475 0.05784302 0.02560284
## 100 100 0.8873298 0.2146413 0.7030686 0.05154811 0.08258908 0.04013377
## 101 101 0.8790121 0.2299558 0.6967405 0.03716780 0.06748775 0.02895051
## 102 102 0.8781525 0.2311377 0.6969265 0.03476901 0.05797698 0.02563416
## 103 103 0.8905289 0.2091553 0.7044055 0.04258219 0.07033540 0.03505715
## 104 104 0.8695932 0.2469401 0.6890519 0.02076765 0.02928223 0.01460585
## 105 105 0.8880504 0.2135613 0.7038346 0.05074847 0.07415235 0.03949009
## 106 106 0.8800272 0.2278115 0.6970377 0.02498742 0.05665633 0.01767124
## 107 107 0.8821651 0.2252208 0.6979574 0.04336607 0.05758522 0.03391988
## 108 108 0.8807501 0.2266920 0.6972507 0.02477364 0.05633373 0.01752880
## 109 109 0.8967270 0.1972821 0.7115335 0.04144454 0.07464579 0.03338275
## 110 110 0.8997634 0.1937672 0.7126542 0.05561697 0.08774994 0.04500401
## 111 111 0.8702263 0.2459552 0.6895464 0.02101232 0.02965806 0.01454195
## 112 112 0.8903524 0.2096682 0.7050336 0.05009782 0.07909699 0.03932298
## 113 113 0.8705723 0.2454088 0.6898242 0.02093825 0.02914713 0.01467843
## 114 114 0.8879206 0.2134248 0.7041379 0.04595357 0.08760947 0.03516943
## 115 115 0.8823528 0.2250199 0.6983838 0.04267116 0.05652946 0.03395477
## 116 116 0.8811961 0.2268070 0.6988290 0.04208795 0.06926264 0.03484253
## 117 117 0.8787213 0.2300289 0.6963509 0.03640613 0.06784676 0.02691773
## 118 118 0.8823546 0.2250464 0.6983996 0.04270534 0.05650324 0.03388883
## 119 119 0.8938430 0.2051221 0.7067402 0.05531859 0.07782329 0.04467267
## 120 120 0.8942301 0.2024635 0.7080069 0.02530332 0.05844446 0.02338602
## 121 121 0.8869207 0.2161184 0.7029837 0.04173290 0.06596414 0.03176377
## 122 122 0.8853800 0.2188253 0.7025751 0.04014896 0.06543125 0.03290895
## 123 123 0.8711735 0.2446217 0.6900063 0.02090185 0.02947937 0.01443953
## 124 124 0.8712499 0.2445088 0.6900996 0.02077998 0.02925185 0.01437353
## 125 125 0.8852838 0.2190458 0.7016937 0.03776990 0.05970707 0.03009853
## 126 126 0.8769383 0.2339106 0.6952785 0.03400644 0.05449170 0.02441856
## 127 127 0.8807512 0.2282340 0.6970068 0.03579438 0.04360425 0.02844577
## 128 128 0.8713120 0.2444134 0.6901454 0.02080916 0.02913392 0.01424772
## 129 129 0.8785592 0.2315281 0.6960533 0.03228396 0.04256308 0.02558950
## 130 130 0.8881676 0.2144256 0.7032615 0.04519594 0.07175614 0.03689767
## 131 131 0.8841984 0.2210339 0.7013286 0.04080732 0.05991611 0.03150366
## 132 132 0.8781843 0.2321274 0.6958763 0.03240911 0.05195299 0.02532678
## 133 133 0.8885599 0.2129868 0.7051763 0.04132372 0.07063382 0.02833477
## 134 134 0.8885267 0.2122287 0.7053880 0.03226337 0.06533686 0.02445234
## 135 135 0.8783801 0.2318316 0.6958231 0.03231969 0.05160722 0.02528119
## 136 136 0.8831134 0.2225367 0.7015132 0.03547883 0.06295876 0.02622033
## 137 137 0.8909363 0.2093461 0.7060760 0.04074586 0.06451993 0.03254968
## 138 138 0.8876283 0.2139765 0.7046909 0.03572435 0.06937936 0.02734170
## 139 139 0.8712273 0.2446337 0.6901116 0.02057606 0.02813964 0.01408198
## 140 140 0.8773690 0.2335695 0.6937533 0.01656964 0.03589462 0.01041944
## 141 141 0.8843899 0.2216278 0.7004931 0.03969360 0.05325514 0.03005578
## 142 142 0.8770043 0.2337341 0.6943001 0.01860452 0.04024808 0.01432655
## 143 143 0.8772930 0.2338241 0.6940832 0.03018234 0.04318959 0.02255753
## 144 144 0.8779113 0.2329412 0.6954075 0.03034209 0.03850299 0.02377100
## 145 145 0.8849571 0.2202284 0.7018672 0.03787237 0.05658704 0.03101406
## 146 146 0.8856490 0.2193301 0.7018079 0.03854409 0.05585330 0.03124995
## 147 147 0.8779845 0.2328427 0.6956111 0.03062902 0.03886213 0.02420119
## 148 148 0.8828540 0.2232540 0.6982054 0.02741000 0.04932691 0.02185099
## 149 149 0.8830156 0.2228023 0.7015840 0.03624636 0.06408037 0.02680838
## 150 150 0.8710704 0.2449394 0.6901420 0.02074007 0.02833590 0.01390146
## 151 151 0.8771149 0.2340161 0.6938959 0.01689200 0.03650757 0.01031599
## 152 152 0.8710439 0.2449781 0.6901303 0.02067980 0.02829817 0.01393167
## 153 153 0.8763071 0.2354355 0.6946701 0.03265122 0.05076213 0.02250645
## 154 154 0.8773978 0.2336162 0.6941162 0.01706643 0.03648034 0.01049713
## 155 155 0.8772313 0.2339998 0.6942587 0.03074284 0.04392757 0.02283432
## 156 156 0.8711705 0.2448291 0.6900400 0.02100605 0.02843497 0.01419098
## 157 157 0.8917184 0.2080722 0.7071321 0.04165064 0.07155356 0.03057671
## 158 158 0.8830328 0.2230756 0.6984236 0.02808380 0.04978640 0.02230207
## 159 159 0.8712442 0.2447382 0.6902155 0.02112477 0.02861427 0.01423198
## 160 160 0.8712939 0.2446732 0.6902229 0.02120927 0.02878279 0.01423106
## 161 161 0.8792484 0.2307976 0.6965309 0.03450789 0.05443992 0.02671256
## 162 162 0.8773659 0.2338292 0.6943017 0.03097357 0.04430455 0.02307861
## 163 163 0.9053818 0.1843795 0.7157695 0.04226576 0.06810901 0.03296967
## 164 164 0.8711626 0.2448654 0.6901244 0.02130914 0.02893649 0.01441282
## 165 165 0.8711896 0.2448254 0.6902037 0.02129250 0.02892562 0.01437011
## 166 166 0.8825132 0.2244600 0.6988889 0.03934787 0.05964149 0.02842343
## 167 167 0.8773387 0.2335165 0.6963069 0.03185273 0.05600972 0.02466964
## 168 168 0.8764585 0.2352330 0.6947786 0.03317726 0.05143325 0.02292088
## 169 169 0.8853609 0.2197275 0.7017771 0.03873814 0.06633716 0.03003136
## 170 170 0.8969943 0.1986235 0.7090437 0.03186406 0.05499626 0.02569715
## 171 171 0.8772715 0.2337150 0.6955156 0.03015274 0.04738763 0.01993269
## 172 172 0.8711308 0.2449391 0.6900102 0.02132361 0.02944756 0.01405425
## 173 173 0.8788238 0.2315335 0.6962921 0.03415416 0.05411603 0.02651226
## 174 174 0.8710595 0.2450524 0.6900226 0.02119134 0.02941022 0.01397852
## 175 175 0.8819765 0.2248788 0.7004738 0.03911016 0.06766862 0.02912259
## 176 176 0.8775050 0.2336354 0.6944693 0.03142886 0.04531519 0.02316051
## 177 177 0.8836776 0.2220384 0.7016318 0.03675402 0.06855830 0.02830100
## 178 178 0.8767563 0.2344055 0.6960170 0.03083001 0.05466123 0.02427947
## 179 179 0.8771774 0.2336021 0.6944936 0.01934757 0.04230079 0.01447292
## 180 180 0.8842062 0.2216586 0.6999682 0.03684584 0.06052832 0.02760136
## 181 181 0.8710621 0.2450610 0.6899539 0.02113441 0.02930182 0.01393759
## 182 182 0.8773183 0.2336710 0.6953280 0.03031958 0.04795258 0.01976529
## 183 183 0.8764649 0.2353879 0.6945290 0.03370409 0.05230365 0.02323804
## 184 184 0.8778125 0.2330155 0.6943328 0.01787912 0.03922403 0.01080818
## 185 185 0.8709901 0.2451699 0.6898404 0.02102933 0.02910482 0.01377659
## 186 186 0.8710149 0.2451310 0.6898602 0.02103722 0.02913694 0.01376368
## 187 187 0.8943494 0.2046563 0.7086186 0.04439265 0.06659718 0.03631762
## 188 188 0.8831106 0.2231985 0.7003239 0.03739352 0.06212067 0.02933691
## 189 189 0.8777498 0.2330968 0.6958261 0.03019375 0.05269710 0.02250059
## 190 190 0.8764103 0.2354932 0.6945957 0.03374887 0.05233494 0.02345894
## 191 191 0.8766569 0.2346453 0.6959247 0.03072530 0.05432256 0.02417583
## 192 192 0.8709712 0.2452138 0.6899392 0.02109606 0.02913178 0.01401495
## 193 193 0.8709630 0.2452153 0.6899571 0.02107376 0.02906165 0.01398495
## 194 194 0.8771244 0.2337058 0.6943960 0.01979326 0.04295587 0.01510961
## 195 195 0.8900946 0.2110887 0.7059017 0.04184141 0.06344153 0.03364339
## 196 196 0.8833638 0.2231604 0.7005634 0.03965294 0.06707302 0.02886321
## 197 197 0.8778698 0.2329310 0.6959447 0.03054640 0.05311989 0.02303724
## 198 198 0.8709362 0.2452538 0.6899037 0.02114948 0.02905340 0.01407753
## 199 199 0.8778068 0.2330295 0.6958969 0.03060173 0.05316317 0.02307152
## 200 200 0.8709257 0.2452752 0.6899232 0.02111645 0.02900423 0.01403420
## 201 201 0.8709900 0.2451787 0.6899355 0.02110991 0.02901857 0.01405752
## 202 202 0.8708800 0.2453426 0.6898542 0.02108169 0.02900399 0.01402446
## 203 203 0.8822449 0.2247976 0.7005981 0.04014678 0.06838389 0.02993296
## 204 204 0.8777869 0.2330835 0.6958779 0.03058122 0.05314272 0.02289808
## 205 205 0.8777755 0.2333443 0.6953865 0.03143003 0.04019148 0.02448087
## 206 206 0.8708463 0.2454086 0.6898147 0.02109298 0.02900501 0.01405162
## 207 207 0.8708843 0.2453488 0.6898349 0.02108793 0.02894228 0.01407714
## 208 208 0.8778978 0.2331569 0.6954029 0.03166306 0.04052815 0.02463750
## 209 209 0.8708897 0.2453462 0.6898105 0.02106403 0.02890307 0.01404738
## 210 210 0.8708898 0.2453374 0.6898011 0.02101205 0.02885188 0.01403132
## 211 211 0.8708959 0.2453254 0.6898031 0.02100373 0.02885301 0.01400295
## 212 212 0.8910485 0.2095971 0.7077067 0.04195313 0.07037863 0.03366007
## 213 213 0.8709249 0.2452777 0.6898214 0.02100077 0.02874207 0.01398078
## 214 214 0.8857540 0.2190424 0.7016905 0.03297921 0.06133378 0.02777038
## 215 215 0.8709433 0.2452467 0.6898224 0.02095978 0.02868184 0.01394823
## 216 216 0.8781318 0.2325133 0.6947492 0.01838755 0.04030732 0.01149088
## 217 217 0.8769393 0.2342917 0.6960485 0.03115040 0.05481293 0.02475400
## 218 218 0.8709594 0.2452213 0.6898122 0.02092089 0.02859895 0.01389152
## 219 219 0.8709504 0.2452379 0.6898225 0.02095061 0.02860147 0.01392855
## 220 220 0.8709595 0.2452215 0.6898295 0.02091165 0.02857674 0.01389363
## 221 221 0.8776376 0.2334405 0.6949424 0.03176891 0.04563396 0.02485092
## 222 222 0.8782940 0.2325723 0.6957195 0.03250050 0.04171031 0.02551631
## 223 223 0.8709661 0.2452133 0.6898494 0.02091606 0.02863118 0.01390926
## 224 224 0.8709725 0.2451994 0.6898613 0.02087188 0.02860715 0.01387450
## 225 225 0.8777332 0.2330876 0.6955773 0.03123433 0.04921745 0.02042175
## 226 226 0.8799875 0.2299521 0.6965204 0.03487411 0.04123035 0.02772406
## 227 227 0.8774499 0.2332170 0.6945491 0.01993798 0.04365775 0.01523257
## 228 228 0.8774275 0.2332623 0.6944787 0.01993739 0.04362411 0.01513202
## 229 229 0.8782547 0.2323516 0.6949476 0.01852453 0.04066171 0.01170167
## 230 230 0.8709578 0.2452244 0.6898649 0.02087034 0.02869888 0.01386252
## 231 231 0.8709710 0.2452039 0.6898742 0.02086966 0.02867474 0.01386523
## 232 232 0.8866944 0.2181125 0.7021300 0.03206324 0.05905111 0.02592700
## 233 233 0.8783169 0.2322635 0.6950739 0.01863309 0.04085134 0.01191995
## 234 234 0.8932242 0.2060625 0.7067224 0.02772155 0.06215890 0.02414876
## 235 235 0.8709550 0.2452287 0.6898716 0.02086803 0.02864109 0.01384866
## 236 236 0.8777029 0.2333690 0.6950907 0.03192672 0.04586394 0.02516570
## 237 237 0.8776781 0.2334071 0.6950696 0.03186372 0.04576035 0.02510013
## 238 238 0.8990969 0.1956639 0.7116227 0.04271014 0.06616901 0.03173134
## 239 239 0.8979575 0.1976363 0.7110696 0.04328589 0.07333348 0.03488553
## 240 240 0.8709482 0.2452370 0.6898676 0.02086412 0.02861708 0.01385710
## nvmax
## 13 13## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## Coefficients of final model:
## (Intercept) x4 x7 x8 x9
## -3.832583e+00 -1.336998e-03 3.502871e-01 1.316519e-02 1.026206e-01
## x10 x11 x16 x17 x21
## 3.532314e-02 6.547159e+06 2.676086e-02 3.808197e-02 3.363089e-03
## stat14 stat98 stat110 sqrt.x18
## -2.807359e-02 1.008003e-01 -9.721343e-02 7.689960e-01Test
if (algo.stepwise.caret == TRUE){
# test.model(model.stepwise, data.test, "Stepwise Selection", draw.limits = TRUE, regsubset = TRUE, id = id, formula = formula)
test.model(model.stepwise, data.test
,method = 'leapSeq',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,id = id
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.615548 -0.383649 0.009481 -0.010402 0.359098 1.565338
## [1] "leapSeq Test MSE: 0.707385129532587"
Stepwise Selection with CV (w/ filtered train)
Train
Test
LASSO (w/ full train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train[,feature.names])
y = data.train[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO (w/ filtered train)
Train
if(algo.LASSO == TRUE){
# Formatting data for GLM net
# you can use model.matrix as well -- model.matrix creates a design (or model) matrix,
# e.g., by expanding factors to a set of dummy variables (depending on the contrasts)
# and expanding interactions similarly.
x = as.matrix(data.train2[,feature.names])
y = data.train2[,label.names]
xtest = as.matrix(data.test[,feature.names])
ytest = data.test[,label.names]
grid=10^seq(10,-2, length =100)
set.seed(1)
model.LASSO=glmnet(x,y,alpha=1, lambda =grid)
cv.out=cv.glmnet(x,y,alpha=1) # alpha=1 performs LASSO
plot(cv.out)
bestlambda<-cv.out$lambda.min # Optimal penalty parameter. You can make this call visually.
print(coef(model.LASSO,s=bestlambda))
}Test
if(algo.LASSO == TRUE){
lasso.pred=predict (model.LASSO ,s=bestlambda ,newx=xtest)
testMSE_LASSO = mean((ytest-lasso.pred)^2)
print (paste("LASSO Test RMSE: ",testMSE_LASSO, sep=""))
plot(ytest,lasso.pred)
}LASSO with CV (w/ full train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0159 on full training set
## glmnet
##
## 6002 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 0.8583978 0.2637735 0.6825531
## 0.01047616 0.8582192 0.2641419 0.6825038
## 0.01097499 0.8580657 0.2644752 0.6824781
## 0.01149757 0.8579246 0.2647982 0.6824721
## 0.01204504 0.8577994 0.2651045 0.6824723
## 0.01261857 0.8577108 0.2653584 0.6825107
## 0.01321941 0.8576488 0.2655775 0.6825836
## 0.01384886 0.8576068 0.2657755 0.6826746
## 0.01450829 0.8575850 0.2659512 0.6827977
## 0.01519911 0.8575631 0.2661425 0.6829169
## 0.01592283 0.8575402 0.2663528 0.6830369
## 0.01668101 0.8575579 0.2665035 0.6831899
## 0.01747528 0.8576093 0.2666076 0.6833627
## 0.01830738 0.8576962 0.2666599 0.6835745
## 0.01917910 0.8578111 0.2666776 0.6838080
## 0.02009233 0.8579626 0.2666430 0.6840772
## 0.02104904 0.8581621 0.2665386 0.6843781
## 0.02205131 0.8584096 0.2663646 0.6847084
## 0.02310130 0.8586993 0.2661309 0.6850740
## 0.02420128 0.8590191 0.2658628 0.6854769
## 0.02535364 0.8593669 0.2655623 0.6859148
## 0.02656088 0.8597560 0.2652082 0.6863776
## 0.02782559 0.8601932 0.2647904 0.6868710
## 0.02915053 0.8606990 0.2642741 0.6874098
## 0.03053856 0.8612658 0.2636714 0.6879965
## 0.03199267 0.8618784 0.2630136 0.6886242
## 0.03351603 0.8625374 0.2623001 0.6892934
## 0.03511192 0.8632386 0.2615414 0.6900032
## 0.03678380 0.8639860 0.2607308 0.6907494
## 0.03853529 0.8647856 0.2598582 0.6915331
## 0.04037017 0.8656354 0.2589276 0.6923396
## 0.04229243 0.8665411 0.2579301 0.6931906
## 0.04430621 0.8674986 0.2568762 0.6940977
## 0.04641589 0.8684883 0.2558134 0.6950462
## 0.04862602 0.8695178 0.2547296 0.6960412
## 0.05094138 0.8705193 0.2537770 0.6970107
## 0.05336699 0.8715130 0.2529194 0.6979802
## 0.05590810 0.8725407 0.2520854 0.6989797
## 0.05857021 0.8736243 0.2512347 0.7000044
## 0.06135907 0.8747403 0.2504340 0.7010479
## 0.06428073 0.8759140 0.2496367 0.7021530
## 0.06734151 0.8771351 0.2488824 0.7033041
## 0.07054802 0.8784181 0.2481466 0.7044968
## 0.07390722 0.8798220 0.2473018 0.7057755
## 0.07742637 0.8813590 0.2463259 0.7071632
## 0.08111308 0.8830424 0.2451926 0.7086541
## 0.08497534 0.8848858 0.2438716 0.7102533
## 0.08902151 0.8869041 0.2423263 0.7119701
## 0.09326033 0.8891134 0.2405115 0.7138243
## 0.09770100 0.8915313 0.2383714 0.7158424
## 0.10235310 0.8941768 0.2358371 0.7180433
## 0.10722672 0.8970708 0.2328230 0.7204080
## 0.11233240 0.9002356 0.2292226 0.7230007
## 0.11768120 0.9036864 0.2249313 0.7258249
## 0.12328467 0.9074457 0.2198015 0.7288903
## 0.12915497 0.9111514 0.2149714 0.7319129
## 0.13530478 0.9148764 0.2102337 0.7349673
## 0.14174742 0.9188285 0.2048585 0.7381875
## 0.14849683 0.9230498 0.1985894 0.7415687
## 0.15556761 0.9276304 0.1909138 0.7451624
## 0.16297508 0.9325867 0.1815206 0.7489916
## 0.17073526 0.9371949 0.1730267 0.7525021
## 0.17886495 0.9414888 0.1655059 0.7556733
## 0.18738174 0.9453354 0.1598535 0.7584425
## 0.19630407 0.9489015 0.1556189 0.7609409
## 0.20565123 0.9528001 0.1500045 0.7636688
## 0.21544347 0.9570595 0.1424704 0.7666746
## 0.22570197 0.9612202 0.1349120 0.7696029
## 0.23644894 0.9652277 0.1275307 0.7724597
## 0.24770764 0.9686993 0.1237665 0.7749128
## 0.25950242 0.9717890 0.1237002 0.7770609
## 0.27185882 0.9751611 0.1236737 0.7794072
## 0.28480359 0.9788450 0.1236737 0.7819715
## 0.29836472 0.9828723 0.1236737 0.7847479
## 0.31257158 0.9872735 0.1236737 0.7878364
## 0.32745492 0.9920814 0.1236737 0.7912441
## 0.34304693 0.9973103 0.1190183 0.7949429
## 0.35938137 0.9997577 NaN 0.7966682
## 0.37649358 0.9997577 NaN 0.7966682
## 0.39442061 0.9997577 NaN 0.7966682
## 0.41320124 0.9997577 NaN 0.7966682
## 0.43287613 0.9997577 NaN 0.7966682
## 0.45348785 0.9997577 NaN 0.7966682
## 0.47508102 0.9997577 NaN 0.7966682
## 0.49770236 0.9997577 NaN 0.7966682
## 0.52140083 0.9997577 NaN 0.7966682
## 0.54622772 0.9997577 NaN 0.7966682
## 0.57223677 0.9997577 NaN 0.7966682
## 0.59948425 0.9997577 NaN 0.7966682
## 0.62802914 0.9997577 NaN 0.7966682
## 0.65793322 0.9997577 NaN 0.7966682
## 0.68926121 0.9997577 NaN 0.7966682
## 0.72208090 0.9997577 NaN 0.7966682
## 0.75646333 0.9997577 NaN 0.7966682
## 0.79248290 0.9997577 NaN 0.7966682
## 0.83021757 0.9997577 NaN 0.7966682
## 0.86974900 0.9997577 NaN 0.7966682
## 0.91116276 0.9997577 NaN 0.7966682
## 0.95454846 0.9997577 NaN 0.7966682
## 1.00000000 0.9997577 NaN 0.7966682
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.01592283.
## alpha lambda
## 11 1 0.01592283## Warning: Removed 23 rows containing missing values (geom_path).## Warning: Removed 23 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.51042 -0.35594 0.00811 -0.01164 0.33226 1.44709
## [1] "glmnet LASSO Test MSE: 0.707600445355111"
LASSO with CV (w/ filtered train)
Train
if (algo.LASSO.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "glmnet"
,subopt = 'LASSO'
,feature.names = feature.names)
model.LASSO.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.01 on full training set
## glmnet
##
## 5694 samples
## 240 predictor
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5123, 5125, 5125, 5124, 5125, 5124, ...
## Resampling results across tuning parameters:
##
## lambda RMSE Rsquared MAE
## 0.01000000 0.7331725 0.3593495 0.6005220
## 0.01047616 0.7332299 0.3593534 0.6005948
## 0.01097499 0.7333077 0.3593320 0.6006897
## 0.01149757 0.7334213 0.3592577 0.6008326
## 0.01204504 0.7335675 0.3591355 0.6010174
## 0.01261857 0.7337529 0.3589545 0.6012509
## 0.01321941 0.7339686 0.3587326 0.6015073
## 0.01384886 0.7342267 0.3584472 0.6017918
## 0.01450829 0.7345383 0.3580746 0.6021218
## 0.01519911 0.7349070 0.3576103 0.6024887
## 0.01592283 0.7352881 0.3571329 0.6028613
## 0.01668101 0.7357166 0.3565825 0.6032685
## 0.01747528 0.7361447 0.3560473 0.6037046
## 0.01830738 0.7366154 0.3554463 0.6041690
## 0.01917910 0.7370378 0.3549538 0.6045901
## 0.02009233 0.7374814 0.3544414 0.6050250
## 0.02104904 0.7379383 0.3539249 0.6054982
## 0.02205131 0.7384331 0.3533564 0.6060026
## 0.02310130 0.7389680 0.3527358 0.6065567
## 0.02420128 0.7395527 0.3520457 0.6071621
## 0.02535364 0.7401109 0.3514306 0.6077318
## 0.02656088 0.7406970 0.3507922 0.6083127
## 0.02782559 0.7412880 0.3501776 0.6088792
## 0.02915053 0.7419234 0.3495113 0.6094720
## 0.03053856 0.7426080 0.3487871 0.6101109
## 0.03199267 0.7433364 0.3480179 0.6107898
## 0.03351603 0.7440806 0.3472675 0.6114746
## 0.03511192 0.7448710 0.3464766 0.6121982
## 0.03678380 0.7456948 0.3456759 0.6129492
## 0.03853529 0.7465846 0.3448005 0.6137529
## 0.04037017 0.7475402 0.3438558 0.6146132
## 0.04229243 0.7485839 0.3427980 0.6155356
## 0.04430621 0.7497339 0.3415910 0.6165337
## 0.04641589 0.7509989 0.3402154 0.6176022
## 0.04862602 0.7523740 0.3386787 0.6187506
## 0.05094138 0.7538713 0.3369551 0.6199898
## 0.05336699 0.7553583 0.3353291 0.6212325
## 0.05590810 0.7568934 0.3336852 0.6225197
## 0.05857021 0.7583447 0.3323339 0.6237288
## 0.06135907 0.7598343 0.3310131 0.6249542
## 0.06428073 0.7613530 0.3297701 0.6262031
## 0.06734151 0.7629878 0.3284144 0.6275325
## 0.07054802 0.7646986 0.3270479 0.6289181
## 0.07390722 0.7665390 0.3255543 0.6303791
## 0.07742637 0.7684157 0.3241897 0.6318606
## 0.08111308 0.7704094 0.3227645 0.6334358
## 0.08497534 0.7725240 0.3213068 0.6350854
## 0.08902151 0.7748218 0.3196521 0.6368533
## 0.09326033 0.7773353 0.3177130 0.6387751
## 0.09770100 0.7800841 0.3154315 0.6408618
## 0.10235310 0.7830893 0.3127361 0.6431281
## 0.10722672 0.7863737 0.3095379 0.6455925
## 0.11233240 0.7899621 0.3057265 0.6482975
## 0.11768120 0.7938812 0.3011639 0.6512601
## 0.12328467 0.7981248 0.2957898 0.6544636
## 0.12915497 0.8026930 0.2895223 0.6579142
## 0.13530478 0.8070543 0.2841350 0.6612514
## 0.14174742 0.8115931 0.2783637 0.6646882
## 0.14849683 0.8163898 0.2718901 0.6682626
## 0.15556761 0.8215785 0.2640388 0.6721144
## 0.16297508 0.8272353 0.2542502 0.6763373
## 0.17073526 0.8333455 0.2422394 0.6808825
## 0.17886495 0.8390177 0.2314923 0.6850103
## 0.18738174 0.8447708 0.2198929 0.6891591
## 0.19630407 0.8494537 0.2132052 0.6923838
## 0.20565123 0.8540099 0.2074269 0.6954779
## 0.21544347 0.8588275 0.2006097 0.6987692
## 0.22570197 0.8640494 0.1915307 0.7023549
## 0.23644894 0.8695398 0.1801569 0.7061149
## 0.24770764 0.8753392 0.1655654 0.7101099
## 0.25950242 0.8796708 0.1598787 0.7130310
## 0.27185882 0.8836352 0.1579728 0.7157315
## 0.28480359 0.8877298 0.1577218 0.7185706
## 0.29836472 0.8921662 0.1577218 0.7216814
## 0.31257158 0.8970100 0.1577218 0.7250606
## 0.32745492 0.9022965 0.1577218 0.7287217
## 0.34304693 0.9080633 0.1577218 0.7327123
## 0.35938137 0.9139336 0.1445719 0.7367976
## 0.37649358 0.9153735 NaN 0.7377998
## 0.39442061 0.9153735 NaN 0.7377998
## 0.41320124 0.9153735 NaN 0.7377998
## 0.43287613 0.9153735 NaN 0.7377998
## 0.45348785 0.9153735 NaN 0.7377998
## 0.47508102 0.9153735 NaN 0.7377998
## 0.49770236 0.9153735 NaN 0.7377998
## 0.52140083 0.9153735 NaN 0.7377998
## 0.54622772 0.9153735 NaN 0.7377998
## 0.57223677 0.9153735 NaN 0.7377998
## 0.59948425 0.9153735 NaN 0.7377998
## 0.62802914 0.9153735 NaN 0.7377998
## 0.65793322 0.9153735 NaN 0.7377998
## 0.68926121 0.9153735 NaN 0.7377998
## 0.72208090 0.9153735 NaN 0.7377998
## 0.75646333 0.9153735 NaN 0.7377998
## 0.79248290 0.9153735 NaN 0.7377998
## 0.83021757 0.9153735 NaN 0.7377998
## 0.86974900 0.9153735 NaN 0.7377998
## 0.91116276 0.9153735 NaN 0.7377998
## 0.95454846 0.9153735 NaN 0.7377998
## 1.00000000 0.9153735 NaN 0.7377998
##
## Tuning parameter 'alpha' was held constant at a value of 1
## RMSE was used to select the optimal model using the smallest value.
## The final values used for the model were alpha = 1 and lambda = 0.01.
## alpha lambda
## 1 1 0.01## Warning: Removed 22 rows containing missing values (geom_path).## Warning: Removed 22 rows containing missing values (geom_point).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LASSO.caret == TRUE){
test.model(model.LASSO.caret, data.test
,method = 'glmnet',subopt = "LASSO"
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.76337 -0.45968 -0.04438 -0.06844 0.30340 1.60102
## [1] "glmnet LASSO Test MSE: 0.710918505671706"
LARS with CV (w/ full train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.394 on full training set
## Least Angle Regression
##
## 6002 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5402, 5401, 5402, 5401, 5402, 5402, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 0.9997577 NaN 0.7966682
## 0.01010101 0.9875243 0.1236737 0.7879771
## 0.02020202 0.9765136 0.1236737 0.7803142
## 0.03030303 0.9667815 0.1238240 0.7735488
## 0.04040404 0.9582779 0.1404488 0.7674966
## 0.05050505 0.9502496 0.1541310 0.7618702
## 0.06060606 0.9429028 0.1627827 0.7567181
## 0.07070707 0.9363359 0.1738892 0.7518754
## 0.08080808 0.9299656 0.1865916 0.7469635
## 0.09090909 0.9238495 0.1973468 0.7421939
## 0.10101010 0.9180614 0.2059358 0.7375603
## 0.11111111 0.9126276 0.2128180 0.7331136
## 0.12121212 0.9075526 0.2196640 0.7289755
## 0.13131313 0.9026844 0.2262642 0.7250094
## 0.14141414 0.8980850 0.2317471 0.7212504
## 0.15151515 0.8937586 0.2362836 0.7176913
## 0.16161616 0.8897092 0.2400233 0.7143168
## 0.17171717 0.8859406 0.2430948 0.7111500
## 0.18181818 0.8824563 0.2456074 0.7081372
## 0.19191919 0.8792599 0.2476534 0.7052713
## 0.20202020 0.8763627 0.2493255 0.7025756
## 0.21212121 0.8737962 0.2510676 0.7001478
## 0.22222222 0.8715859 0.2528262 0.6980518
## 0.23232323 0.8696688 0.2545800 0.6961798
## 0.24242424 0.8678406 0.2565180 0.6944202
## 0.25252525 0.8660831 0.2584592 0.6927507
## 0.26262626 0.8645403 0.2601276 0.6912835
## 0.27272727 0.8631148 0.2616905 0.6898801
## 0.28282828 0.8618307 0.2630866 0.6885658
## 0.29292929 0.8607883 0.2641765 0.6874904
## 0.30303030 0.8599638 0.2649954 0.6865863
## 0.31313131 0.8592946 0.2656298 0.6858032
## 0.32323232 0.8587742 0.2660952 0.6851616
## 0.33333333 0.8583835 0.2664127 0.6846502
## 0.34343434 0.8580865 0.2666087 0.6842575
## 0.35353535 0.8578654 0.2667185 0.6839282
## 0.36363636 0.8577093 0.2667412 0.6836486
## 0.37373737 0.8576031 0.2667115 0.6834201
## 0.38383838 0.8575244 0.2666564 0.6832159
## 0.39393939 0.8575151 0.2664933 0.6830837
## 0.40404040 0.8575360 0.2662914 0.6829887
## 0.41414141 0.8575613 0.2660984 0.6828968
## 0.42424242 0.8575769 0.2659424 0.6827868
## 0.43434343 0.8575861 0.2658114 0.6826788
## 0.44444444 0.8576127 0.2656606 0.6825920
## 0.45454545 0.8576608 0.2654829 0.6825366
## 0.46464646 0.8577169 0.2653014 0.6824883
## 0.47474747 0.8578013 0.2650788 0.6824753
## 0.48484848 0.8579019 0.2648344 0.6824791
## 0.49494949 0.8579995 0.2646022 0.6824744
## 0.50505051 0.8581102 0.2643549 0.6824862
## 0.51515152 0.8582357 0.2640884 0.6825107
## 0.52525253 0.8583665 0.2638174 0.6825472
## 0.53535354 0.8585177 0.2635165 0.6826061
## 0.54545455 0.8586749 0.2632119 0.6826698
## 0.55555556 0.8588487 0.2628840 0.6827409
## 0.56565657 0.8590269 0.2625534 0.6828203
## 0.57575758 0.8592184 0.2622051 0.6829151
## 0.58585859 0.8594256 0.2618352 0.6830220
## 0.59595960 0.8596415 0.2614565 0.6831306
## 0.60606061 0.8598630 0.2610733 0.6832448
## 0.61616162 0.8600844 0.2606957 0.6833602
## 0.62626263 0.8603086 0.2603177 0.6834762
## 0.63636364 0.8605392 0.2599327 0.6835919
## 0.64646465 0.8607776 0.2595385 0.6837162
## 0.65656566 0.8610216 0.2591395 0.6838488
## 0.66666667 0.8612711 0.2587363 0.6839791
## 0.67676768 0.8615256 0.2583298 0.6841136
## 0.68686869 0.8617832 0.2579219 0.6842508
## 0.69696970 0.8620422 0.2575164 0.6843960
## 0.70707071 0.8623020 0.2571143 0.6845402
## 0.71717172 0.8625705 0.2567019 0.6846882
## 0.72727273 0.8628463 0.2562813 0.6848493
## 0.73737374 0.8631256 0.2558586 0.6850111
## 0.74747475 0.8634059 0.2554388 0.6851731
## 0.75757576 0.8636859 0.2550231 0.6853326
## 0.76767677 0.8639656 0.2546116 0.6854916
## 0.77777778 0.8642435 0.2542067 0.6856517
## 0.78787879 0.8645245 0.2537997 0.6858145
## 0.79797980 0.8647989 0.2534059 0.6859712
## 0.80808081 0.8650728 0.2530165 0.6861273
## 0.81818182 0.8653501 0.2526256 0.6862904
## 0.82828283 0.8656304 0.2522339 0.6864600
## 0.83838384 0.8659110 0.2518447 0.6866273
## 0.84848485 0.8661970 0.2514506 0.6868015
## 0.85858586 0.8664907 0.2510482 0.6869845
## 0.86868687 0.8667910 0.2506394 0.6871711
## 0.87878788 0.8670918 0.2502337 0.6873559
## 0.88888889 0.8673971 0.2498238 0.6875437
## 0.89898990 0.8677029 0.2494164 0.6877322
## 0.90909091 0.8680107 0.2490090 0.6879263
## 0.91919192 0.8683266 0.2485922 0.6881262
## 0.92929293 0.8686414 0.2481810 0.6883256
## 0.93939394 0.8689606 0.2477662 0.6885344
## 0.94949495 0.8692861 0.2473455 0.6887517
## 0.95959596 0.8696115 0.2469282 0.6889698
## 0.96969697 0.8699367 0.2465148 0.6891865
## 0.97979798 0.8702677 0.2460950 0.6894048
## 0.98989899 0.8706053 0.2456683 0.6896323
## 1.00000000 0.8709482 0.2452370 0.6898676
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.3939394.
## fraction
## 40 0.3939394## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.503371 -0.354374 0.007588 -0.011675 0.331160 1.441923
## [1] "lars Test MSE: 0.707749854584791"
LARS with CV (w/ filtered train)
Train
if (algo.LARS.caret == TRUE){
set.seed(1)
returned = train.caret.glmselect(formula = formula
,data = data.train2
,method = "lars"
,subopt = 'NULL'
,feature.names = feature.names)
model.LARS.caret = returned$model
}## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting fraction = 0.576 on full training set
## Least Angle Regression
##
## 5694 samples
## 240 predictor
##
## Pre-processing: centered (240), scaled (240)
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 5123, 5125, 5125, 5124, 5125, 5124, ...
## Resampling results across tuning parameters:
##
## fraction RMSE Rsquared MAE
## 0.00000000 0.9153735 NaN 0.7377998
## 0.01010101 0.9006821 0.1577218 0.7275815
## 0.02020202 0.8874568 0.1577218 0.7183732
## 0.03030303 0.8759006 0.1622088 0.7104946
## 0.04040404 0.8652594 0.1893790 0.7031461
## 0.05050505 0.8553702 0.2057376 0.6963500
## 0.06060606 0.8463094 0.2167572 0.6901862
## 0.07070707 0.8380529 0.2323114 0.6842997
## 0.08080808 0.8301022 0.2488924 0.6784791
## 0.09090909 0.8224672 0.2626555 0.6728261
## 0.10101010 0.8152303 0.2735271 0.6674487
## 0.11111111 0.8084328 0.2820912 0.6623372
## 0.12121212 0.8021291 0.2900623 0.6575262
## 0.13131313 0.7960443 0.2984078 0.6529294
## 0.14141414 0.7902927 0.3052923 0.6485917
## 0.15151515 0.7848816 0.3109489 0.6445159
## 0.16161616 0.7798181 0.3155794 0.6407058
## 0.17171717 0.7751091 0.3193554 0.6371169
## 0.18181818 0.7707609 0.3224212 0.6337605
## 0.19191919 0.7668587 0.3252669 0.6306573
## 0.20202020 0.7632711 0.3281513 0.6277667
## 0.21212121 0.7600537 0.3307854 0.6251485
## 0.22222222 0.7572915 0.3332410 0.6228605
## 0.23232323 0.7547146 0.3359146 0.6206923
## 0.24242424 0.7521914 0.3387610 0.6185778
## 0.25252525 0.7498190 0.3413782 0.6165999
## 0.26262626 0.7476622 0.3436339 0.6147179
## 0.27272727 0.7458432 0.3454754 0.6131135
## 0.28282828 0.7442617 0.3470989 0.6117116
## 0.29292929 0.7429255 0.3484445 0.6104800
## 0.30303030 0.7417118 0.3497619 0.6093282
## 0.31313131 0.7407206 0.3508455 0.6083739
## 0.32323232 0.7398993 0.3517279 0.6075589
## 0.33333333 0.7391542 0.3525701 0.6067868
## 0.34343434 0.7384721 0.3533786 0.6060754
## 0.35353535 0.7379024 0.3540349 0.6054818
## 0.36363636 0.7374043 0.3546050 0.6049874
## 0.37373737 0.7369549 0.3551265 0.6045509
## 0.38383838 0.7365454 0.3556071 0.6041385
## 0.39393939 0.7361494 0.3560926 0.6037269
## 0.40404040 0.7357668 0.3565767 0.6033361
## 0.41414141 0.7354102 0.3570317 0.6030009
## 0.42424242 0.7350802 0.3574509 0.6026890
## 0.43434343 0.7347805 0.3578244 0.6023963
## 0.44444444 0.7345064 0.3581607 0.6021204
## 0.45454545 0.7342702 0.3584362 0.6018680
## 0.46464646 0.7340541 0.3586815 0.6016305
## 0.47474747 0.7338732 0.3588735 0.6014213
## 0.48484848 0.7337094 0.3590432 0.6012280
## 0.49494949 0.7335663 0.3591830 0.6010497
## 0.50505051 0.7334451 0.3592894 0.6008950
## 0.51515152 0.7333603 0.3593365 0.6007825
## 0.52525253 0.7332904 0.3593643 0.6006903
## 0.53535354 0.7332246 0.3593909 0.6006167
## 0.54545455 0.7331747 0.3593956 0.6005600
## 0.55555556 0.7331448 0.3593713 0.6005092
## 0.56565657 0.7331287 0.3593284 0.6004594
## 0.57575758 0.7331146 0.3592871 0.6004174
## 0.58585859 0.7331164 0.3592237 0.6003836
## 0.59595960 0.7331342 0.3591373 0.6003570
## 0.60606061 0.7331666 0.3590313 0.6003358
## 0.61616162 0.7332165 0.3589000 0.6003194
## 0.62626263 0.7332893 0.3587344 0.6003226
## 0.63636364 0.7333815 0.3585403 0.6003471
## 0.64646465 0.7335001 0.3583052 0.6003963
## 0.65656566 0.7336417 0.3580355 0.6004619
## 0.66666667 0.7338002 0.3577424 0.6005460
## 0.67676768 0.7339630 0.3574491 0.6006296
## 0.68686869 0.7341327 0.3571490 0.6007165
## 0.69696970 0.7343146 0.3568326 0.6008059
## 0.70707071 0.7345046 0.3565064 0.6009014
## 0.71717172 0.7347016 0.3561720 0.6010058
## 0.72727273 0.7349134 0.3558170 0.6011209
## 0.73737374 0.7351405 0.3554409 0.6012444
## 0.74747475 0.7353857 0.3550391 0.6013843
## 0.75757576 0.7356385 0.3546293 0.6015271
## 0.76767677 0.7358969 0.3542150 0.6016737
## 0.77777778 0.7361559 0.3538050 0.6018144
## 0.78787879 0.7364231 0.3533861 0.6019585
## 0.79797980 0.7367015 0.3529538 0.6021141
## 0.80808081 0.7369903 0.3525096 0.6022790
## 0.81818182 0.7372817 0.3520659 0.6024450
## 0.82828283 0.7375831 0.3516105 0.6026157
## 0.83838384 0.7378935 0.3511451 0.6027956
## 0.84848485 0.7382092 0.3506753 0.6029821
## 0.85858586 0.7385344 0.3501952 0.6031797
## 0.86868687 0.7388747 0.3496946 0.6033876
## 0.87878788 0.7392271 0.3491786 0.6036053
## 0.88888889 0.7395845 0.3486591 0.6038251
## 0.89898990 0.7399407 0.3481457 0.6040447
## 0.90909091 0.7402961 0.3476373 0.6042615
## 0.91919192 0.7406562 0.3471253 0.6044821
## 0.92929293 0.7410235 0.3466057 0.6047155
## 0.93939394 0.7413951 0.3460838 0.6049545
## 0.94949495 0.7417708 0.3455597 0.6051926
## 0.95959596 0.7421498 0.3450344 0.6054385
## 0.96969697 0.7425328 0.3445070 0.6056865
## 0.97979798 0.7429222 0.3439742 0.6059398
## 0.98989899 0.7433154 0.3434400 0.6061995
## 1.00000000 0.7437119 0.3429053 0.6064635
##
## RMSE was used to select the optimal model using the smallest value.
## The final value used for the model was fraction = 0.5757576.
## fraction
## 58 0.5757576## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Test
if (algo.LARS.caret == TRUE){
test.model(model.LARS.caret, data.test
,method = 'lars',subopt = NULL
,formula = formula, feature.names = feature.names, label.names = label.names
,draw.limits = TRUE)
}## [1] "Summary of predicted values: "
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -1.77771 -0.45970 -0.04139 -0.06823 0.30320 1.62919
## [1] "lars Test MSE: 0.711598924914466"